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The Impact of Big Data Analytics Across Industries

Big data has long evolved from being confined to IT sectors to becoming a business imperative. In 2018, the International Data Corporation (IDC) forecasted that global revenue for big data and business analytics solutions would reach $60 billion in 2022 with a compound annual growth rate of 11.9% from 2017 to 2022. However, the IDC’s latest Spending Guide placed that figure at $215.7 billion in 2021. 

As companies continue to find new ways to better leverage the massive amounts of data being collected every moment to enable solutions and retain a competitive edge, we take a look at several case studies of how big data is applied in five different industries.  

 

Human Resources: Driving business performance via people analytics 

 

A McKinsey case study details a major restaurant chain with thousands of outlets around the world looking to improve customer satisfaction and grow revenue. Business leaders believe could be done by solving the company’s high staff turnover problem by better understanding people. 

New and existing data were collected from individuals, shifts, and restaurants across the US market including the financial and operational performance of each outlet. Some points considered include personality traits of employees, day-to-day management practices, as well as staff interactions and behaviors.  

The more than 10,000 data points were used to build a series of models to determine the relationship, if any, between the desired outcomes and drivers. The model was used to test over 100 hypotheses, many of which were posited by senior management based on their own observations and instincts from years of experience. 

Noting that some of the hypotheses were proven while others were disproven, McKinsey reported: “This part of the exercise proved to be especially powerful, confronting senior individuals with evidence that in some cases contradicted deeply held and often conflicting instincts about what drives success.” 

Ultimately, the analysis revealed four insights that have gone on to inform the company’s day-to-day people management in its pilot market.  

Just four months in, the company experienced: 

  • Over 100% increase in customer satisfaction scores 
  • 30 seconds improvement in speed of service  
  • Decrease in attrition for new joiners 
  • 5% increase in sales  
 

Supply Chain: Improving cost and service efficiency 

 

A multi-location manufacturer sought to mine its vast library of inventory, shipping, and freight billing data to find ways to improve spending while maintaining service levels. They also wanted to identify opportunities for better inventory management, trip reductions, and order consolidation.  

Using available data, the solution provider created an integrated data management and analytics platform. This was supplemented by a custom order management algorithm.  

The system helped the company consolidate orders heading out to the same location in order to ship them out in one go, thereby reducing congestion at the shipping dock and reducing freight costs by 25%.   

Predictive analysis applied to the company’s supply chain management also led to: 

  • 10% increase in shipping capacity 
  • Improved service-level metrics 
  • 10% decline in inventory levels  
  • Less shipment backlog during peak seasons 
  • Clarity on freight spend drivers 
 

Healthcare: Effective screening and treatment of diseases 

 

In China, there has been a rise in cerebrovascular diseases such as strokes. In response, the government launched a Healthy China 2020 plan aimed at improving public health. 

Following that, medical professionals investigated how best to treat strokes and related medical conditions by identifying three key areas: accurate screenings, precise treatments, and meticulous rehabilitation.  

They wanted a more effective way to analyze data than just using the traditional manual paperwork system, which was not scalable.  

Partnering with IBM, the Shanghai Changjang Science and Technology Department along with China’s top three hospitals developed an intelligent stroke assessment and management platform. The AI-enabled platform analyzes patient information, applies a screening model, and compares these details with known risk factors.  

Patients that have been identified as high-risk are then channeled to the appropriate physician with treatment recommendations and corresponding probabilities of success.  

This application of big data analysis led to: 

  • 15% improvement in diagnostic accuracy of stroke risks in patients 
  • 80.89% accuracy in predicting treatment outcomes 
  • Scaling risk screenings to cover a larger population and encouraging early treatment 
 

Financial Services: Post-trade analysis 

 

The National Bank of Canada’s Global Equity Derivatives Group (GED) provides trading solutions that manage securities such as stocks, futures, funds, and options. It collects and processes a high volume of stock-market financial data, but faces a challenge when it comes to data analysis.  

The bank sought to find a more effective and scalable way to process and analyze structures and unstructured data, as well as historical data, in order to develop a better analytical solution.  

Using an open-source big data processing framework and moving its processes to the cloud allowed the bank to achieve its goal of scalability. The GED was able to analyze hundreds of terabytes of trade and historical data. This now enables their business analysts to conduct quicker post-trade analysis.  

Big data analysis allowed the bank to: 

  • Reduce post-trade analysis process from a few weeks to a few hours 
  • More robust post-trade analysis 
  • Improved trading operations 
  • Increase revenue 
  • Increased customer satisfaction 
 

Manufacturing: Predicting Equipment Anomalies 

 

A major manufacturing company looked to deploy digital twin technology to make manufacturing more flexible and efficient. The company, which was struggling to meet its production targets due to unscheduled downtime, created an IoT sensor-enabled digital copy of its critical equipment to predict potential anomalies and maintain the flow of its assembly lines. 

Falling short of its production target also meant that the company faced increased operating costs, customer dissatisfaction, and lost market share to its competitors.  

Applying IoT-supported digital twins technology allowed the company to collect real-time data. When analyzed with other data sets – historical and maintenance-related – the company was able to remotely monitor and assess its physical assets.  

The ML-based algorithm sifted through plenty of data to help detect abnormal equipment behavior and proactively suggested corrective actions before failure. This led to: 

  • 100% achievement of production target 
  • 25% reduction in operation costs 
  • 54% increase in profit margins 
  • Timely product delivery 
  • Higher customer satisfaction and increased market share 
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Alin Kalam: Nurturing Growth and Innovation Through Data, AI, and Sustainability

The IT industry continues to grow and shift rapidly due to the pandemic and CIOs are constantly on the lookout for ways to foster and adopt new technologies into their organization. Whether it is sustainable transformations or implementing AI, change is necessary.

As the Head of International Market Intelligence & Data Strategy for UNIQA international, Alin Kalam shares with us his insights on the need for agility through AI, achieving business competence, and nurturing innovation.

 
Be part of Aurora Live, an exclusive members-only platform that’s tailored for CxOs seekng the latest industry insights, high-level networking opportunties, and more.
 

Finding Agility in Artificial Intelligence and Overcoming Disruptions

Businesses and IT leaders today need to be quicker to respond to the ever-changing landscape of their industry and overcome disruptions. Whether it’s to implement hybrid workplace models or to incorporate new technologies such as artificial intelligence and data analytics, there is a definite need for CIOs to strategize.

Kalam shares his insights on the key challenges that CIOs need to be aware of when incorporating new technology and how to effectively transition towards data-driven business models.

 

What are the key challenges for CIOs who are trying to adopt new technologies especially in the AI field?

 

Surely one of the major challenges of establishing AI technologies in companies is lack of trust and also limited knowledge existing. On the technical side, I see the IT productionizing & operational issues arising since 2019. 

Often it is not the number of best practices, that lack but the ability to align market circumstances with existing technologies with own true business needs. Therefore, I see the cultivation of AI-driven innovation much more as a strategic challenge nowadays than only a technological one.

 

What should CIOs be aware of in the transition towards data-driven business models that serve dehumanization of critical business fields?

 

On the one hand, dehumanization must be done quickly to address short-term issues e.g. through the implementation of RPA or AI products to combat challenges caused by Covid, and on the other hand, CIOs must balance strategically what and where they are automatizing/dehumanizing. I already have seen examples of cost reduction projects through dehumanization that are creating huge strategic risks for companies in the long run. 

For sure there will be someday an “after Covid” and using the current crisis as scapegoat for cost-cutting only without putting the focus on the product portfolio, customer needs, and above all operational risks of IT systems, can become a huge source of risk. 

Here I rather appeal to strategic long-term aspects than short-termed gains only and to address this concern CIOs must become business-driven more than ever!

 

The Need For Sustainability and Competent Business Intelligence

Companies were forced to change their policies, behaviors, and business strategy due to the prolonged coronavirus pandemic. The recent COP26 climate conference showed that companies are committed to making sustainable-focused organizational changes.

For Kalam, the need for sustainability in IT is clear highlights the challenges that many are still facing, in addition to incorporating competent business intelligence to ensure sustainable growth. 

 

Sustainable transformation in the IT & innovation field has become a key topic for upcoming years. What are the specific areas of action for CIOs in this field?

 

For sure sustainability as a topic is here to stay! Not only do we have the macro aspects of it addressing the major concerns of our time, but it has become also a business driver in so many sectors. 

With my initiated project Sustainista I, therefore, have tried to interconnect companies with the scientific community ensuring exchanging of data, know-how, best practices, and transparency. The biggest challenge in this field is the lack of market and scientific standards at the same time. ESGs might be known to many of us but breaking down its info business actions according to standard approaches/processes is the biggest challenge!

In an ideal world, CIOs and related roles are taking ownership of this topic and driving it to doable tasks, otherwise, I am afraid to see sustainability just as a cosmetic and marketing label without a true impact on business and how we do things.

A particular starting point is to understand macro goals as an organization and break them down to a very data level in organizations delivering measures and related actions with the help of existing data. Many companies I know from various sectors have started with external data sets 1st to deliver quick success that can feed this long-term topic.

 

How would you advise companies who are still struggling to incorporate Business Intelligence?

 

Here I clearly follow the storyline of failing fast succeed sooner. Instead of propagating a piece of technology IT must build a bridge with business and deliver quick wins. Even now I am often devastated whenever I see only PDFs and Excel Sheets with numbers/KPIs that do not reflect the fast reality of our businesses and data-driven decision-making across borders! 

Major issues companies face are data quality, integrity, and security issues. CIOs are hereby in the role of process enablers. Instead of being only technology-driven often the implementation of BI must be done in a joint-venture manner.

 

Ensuring Growth Through Data and Overcoming Legacy Challenges

One of the biggest hurdles for digital transformation efforts still stems from legacy systems that are often outdated and not integrated with modern solutions for business uses. Despite the fact that modernizing legacy IT systems is required for businesses to ensure growth, IT leaders are still faced with roadblocks and challenges.

For Kalam, however, legacy systems are not necessarily the main roadblock as it once was. Instead, the focus now for CIOs should be to apply best practices during data-driven business transformation and simplify their approach to nurturing experimentation.

 

With regards to data-driven business models, what are the best practices that CIOs and IT leaders need to keep in mind? 

 

In a matter of fact, the approach of data-driven business transformation is everything but only data-centric! It covers the end-to-end processes of entire product lines and the strategic setup of a company. After many years of data harmonization/migration projects, companies often find out their undone homework regarding “creating true business values to the company itself and its customers”. 

I myself often propagate the term “no business value without data, no data without a business case”. Between this symbiotic relationship lies the true success of transformation efforts. 

Aside from this core topic I often miss the foresight of wisdom! It means seeing the potential of data not only in core businesses but its extensions and added capacities. In my objective point of view, this foresight of wisdom and true added potential is often the key success factor to many.

 

One of the main challenges for organizations is to overcome legacy infrastructure. How can CIOs overcome the legacy obstacle? What are the skills and mindset needed to promote modernization for an organization?

 

To be honest I really do not see legacy infrastructure as the biggest road-blocker anymore. Especially throughout the last decade, there have been so many progressions in simplifications of legacy systems, that I have become more optimistic on that end out of my own experiences! 

I can´t remember when I have seen companies e.g. migrating legacy data systems into new all-in-one and all-ruling superior DWH, Data Lake, etc. Instead of searching for the holy grail, we have become more realistic about using data where they are at their best and being created. 

This Data Mesh approach has become a blueprint for software solutions as well just as agility was cultivated from the IT/Software world into day-to-day business & project management. But this process has just begun a couple of years ago, the community yet does not have a buzzword, but hey, never say never…!

 

Innovation and experimentation are at the heart of data-driven business models. How does one nurture an environment that promotes experimentation within their organization?

 

I rigorously follow the principle of K.I.S.S (Keep it simple, stupid) in the incubation phase of innovation projects. Instead of talking only and selling in this phase, organizations should apply these principles, aside from a minimum set-up of governance, risk mitigation process regarding GDPR, privacy, organizational risks, etc., and allow experimentation. 

Here the old wisdom of “too many rules & regulations kill true innovation & creativity” should be applied. 

If the internal challenges are too big, often I have guided companies and leading bodies into the world of entrepreneurship. 

The most successful CIOs & IT managers are those who run new innovation ideas or projects as a starting business operating from day 1. This can be a guarantee of nursing the true nature of innovation when nothing else is working.

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Karin Immenroth: Developing Competency In a Data-Driven Business Culture

The advent of readily available data has fostered a new era of fact-based innovations in corporations, where exploring innovations and new systems can be backed up with empirical evidence. And with the disruption caused by COVID-19, there is accelerated adoption in data technology.

So why is it hard for businesses to adopt data as part of their organizational structure?

The biggest obstacles do not stem from the technical side of things; it’s about the culture. In this interview, Chief Data and Analytics Officer for RTL Deutschland Karin Immenroth shares with us how a business needs to transition into a data-driven culture and the approaches that a modern chief data officer (CDO) needs to adopt in today’s digital landscape.

 

The New Landscape of Data Culture

Over the past decade, data has steadily become an influential factor for decision-making processes. Especially in the past year where almost 60% of the global population is constantly online, businesses are looking into data analytics to better understand their customers and employees.

As with the aftereffects of the pandemic and the changing demands of today’s market, Immenroth highlights how the role of the data officer today has changed significantly while pointing out the underlying driving force for data transformation.

 

How has the role of the Chief Data Officers (CDO) changed and what challenges do they face in a post-pandemic market?

Companies didn’t have Chief Data and Analytics Officers ten years ago. That role didn’t exist yet. But because the market is changing dramatically due to progressive digitalization, “Data” as a topic is becoming more and more important. 

The biggest challenge, however, is cultural – it is not enough for a central data area to drive the cultural change, rather the entire company must start working in a data-centric way. 

The DATA Alliance is the central catalyst for RTL Deutschland on its way to becoming a content, tech, and data powerhouse. The pandemic has permanently changed the way we work. 

For us, as the DATA Alliance, the development surrounding the “mobile office” is very positive, as it means we can now work across Germany and in a completely flexible way. This helps us find and attract the best talent in the German market.

 

Why are companies still struggling to implement data competency and how has the pandemic affected their hesitancy towards adopting data culture?

We are in the middle of a cultural change, transitioning into a data-driven company. 

RTL Deutschland is a company with over 3,000 employees – a cultural change doesn’t happen overnight. It takes time, and it’s also important to have a few lighthouse projects that carry the topic of “data” into the organization and help spread awareness. 

We must make it easier for our colleagues throughout the company to access data, support them in interpreting data, and, of course, show them how to make better decisions based on this data. 

Just like the motto goes, “Use data, be better”. The pandemic has been a positive and driving force behind our cultural change – greater digitization has also brought the processing and implementation of data more broadly into society.

 

Developing and Simplifying Data For Organizations

Without a solid foundation for data culture, businesses will often miss out on the chance to fully utilize the data they’ve collected, or even encounter issues with data consistency or internal processes.

Deloitte reports that only 21% of the global workforce is confident in their data literacy skills. And with 70% of organizations expected to shift to new analytics techniques known as “small data” and “wide data”, businesses that are not data literate will get left behind.

Immenroth dives into how the leadership in RTL Deutschland has steered the company towards developing its analytics sector and advises those who are still trying to find success in building a data-competent organization. 

 

What can those in leadership roles do to improve data literacy within their organizations?

We have launched various projects that help our colleagues make better use of data for themselves and their day-to-day work. 

These are, for example, projects like our Reporting Center or our quota tool, Key Vision. We also support various stakeholders in the company by building data products and decision-support tools for their businesses. 

At the moment we are particularly active in the marketing, content, and digital sectors. And it’s also crucial for us to continue developing in the analytics sector, as it will enable us to make even better use of the treasure trove that is data analysis.

 

For companies and organizations that are struggling to find success in data, what key metrics and best practices should they focus on to drive the importance of data?

Our experience shows that it makes sense not to overcomplicate the initial steps. Very exciting and useful insights can often be found in simple descriptive data metrics. 

If you then go one step further and use analytics or even machine learning, data science, etc., you’ll often find unexpected results and insights that have been “fleshed out” by the data. 

I recommend a good dose of courage to use unconventional methods and approaches – we have had very positive experiences here and have been very pleasantly surprised on more than one occasion.

 

Starting Small and Establishing Data Competence Centers

In 2021, global big data and business analytics was forecasted to grow to $215 billion while connected IoT devices are expected to create 79.4ZB of data by 2025. 

With global economies adopting data analytics at an accelerated pace, businesses might be tempted to “go big” with investments in a data-driven culture. Immenroth believes that CDOs and organizations should do the opposite instead while building on Data Competence Centers to kickstart their digital transformation.  

 

In the pursuit of a data-driven culture, what pitfalls or common mistakes should CDOs or organizations be aware of?

More doesn’t always mean better. My experience is that it’s best and most sensible to start “small” and then expand gradually. In concrete terms – it is better to always start with a small proof of concept and then decide whether something bigger can emerge from it.

Fail fast and have the courage to make and admit mistakes… This is the best way to learn and then use what you’ve learned in your organization.

 

How would you advise CDOs or data leaders who want to seamlessly integrate competence centers?

My recommendation is to look at where topics related to data are anchored throughout the company. 

Then, based on that, you can build the core for the so-called Competence Center. It is advisable to define central topics and make them the heart of the Competence Center, and it is also fundamentally important that enough “data” ambassadors are distributed throughout the company in the areas correlating to each topic. 

In my opinion, it’s this balance that counts. In any case, our experience shows that a central Data Competence Center can be a very successful catalyst for the transformation of a company.

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Marco Hoppenbrouwer: Fueling Growth Through Data-Driven Culture

With remote work expected to become a mainstay in the foreseeable future, IT and business leaders are looking at new ways to ensure that their employees are equipped with the necessary critical insights needed to make business decisions.

One of the key approaches is to develop a data-driven culture: the utilization of emerging technologies to drive business value, pushing an organization to be insight-driven as opposed to gut-feeling and operating in the dark.

Marco Hoppenbrouwer, Chief Data Officer for Global Functions & Finance at Shell, understands the value of data to drive value for businesses in today’s modern landscape. In this interview, Hoppenbrouwer shares his insights on how the chief data officer (CDO) role has evolved and why a data-driven culture is a necessity for corporations.

 

The Power of Digital and Data and The CDO’s Role

To compete in an age of rapid acceleration, companies need to be data-driven, but the transition from a feeling- to a fact-based organization is not an easy path. With only 24% of companies truly fostering data-driven cultures, it is up to the CDO to push the initiative of cultivating data technology in a business.

However, before CDOs can achieve that, Hoppenbrouwer points out why the components of Digital and Data are important for a business and what the focus needs to be for CDOs to streamline the transition towards a fact-based organization.

 

How has the role of the chief data officer (CDO) evolved in today’s data-driven culture?

 

Let me first set the scene as to why digital and data are so important for any business and one cannot do without the other:

The energy transition and digitalization are two mega-trends that affect the world in the coming decades. Both are expected to have a profound impact on the way everyone lives their lives.

Digital technologies can play a key role in the transition to a lower-carbon future. Furthermore, we see a rapid increase in digital products, services, and processes coupled with increasing expectations for a seamless digital experience from both our customers and employees. 

Digital is not new, but what’s different is the availability of technology, data, and capabilities that are growing at an exponential rate. Digital is also one of the few processes that require quality data as input to be successful. 

I’ve seen projects fail because a Proof of Concept was successful as it was run on manipulated datasets but this was not the reality in the field when the solution was to be deployed.

Maximizing the benefits of digital technology is heavily dependent on the readiness of an organization and its workforce, meaning:

  • It is key to upskill the workforce with new tech skills
  • The entire organization needs to be data-savvy

This also means that if the organization cannot keep up, it will rapidly be taken over by a competitor that leverages digital technologies and can deliver a more compelling value proposition faster and at a lower price point. A good example of digital disruption is SpaceX which disrupted the entire launch industry.

As to how this answers your question, the role of the CDO has changed from an information & compliance management role into a strategic value generator role. With digitalization and the emergence of the CDO, data is now at the forefront and is seen as a key-value driver that drives business outcomes.

 

What role should the CDO play in streamlining the transition to a fact-based organization?

 

There are three key areas the CDO should focus on:

  1. Accelerate digitalization and drive business value from data, meaning:
    • CDO knows how data enables the business strategy and what value it can drive
    • CDO knows what data is needed, who owns it, where it’s mastered, whether it can be trusted, and how it can be accessed
    • CDO formalizes roles & responsibilities for Data Management
  2. Increasing organization’s data literacy so that:
    • employees understand the importance and their role in data management, including data quality beyond their line of business. For example: if the quality of data you create is not good enough for downstream usage by another line of business, you create a problem in the value chain.
    • employees have the technical skills to drive value from data through citizen developer tools such as the Microsoft Power Platform. This can be achieved through developing role-based learning paths, setting up a community of practices for sharing key data-related best practices, and running DIY boot camps or Hackathons.
  3. The CDO should strive for data-based decision-making by ensuring that the required analytics & insights are timely available in the decision-making process
 

Eyeing The Potential and Opportunities of Data-Driven Culture

The field of data analytics has consistently grown, in terms of acceptance and importance, and will play a critical role as a decision-making resource for executives in modern companies. 

Gartner predicts that by 2024, at least 30% of organizations will invest in data and analytics platforms, increasing their business impact for trusted insights and encouraging new efficiencies. As such, CDOs must take the initiative in fostering data technology as an organizational asset for digital transformation.

But, what are the challenges and how should CDOs approach this transition?

Hoppenbrouwer delves into the main points of how the CDOs should facilitate the data strategy for an organization, and the perspective needed to overcome the challenges of digitalization in a post-pandemic market. 

 

With data having the potential to transform functions into a high business impact model, what initiatives should the CDO take to help this transition?

 

Ensure that there is a data strategy for each line of business. This helps pave a clear roadmap for the usage of data, the business value it enables, and the capabilities required to deliver this value.

Secondly, businesses will need to get the data into good shape, meaning:

  • Identify data ownership and resolve where ownership is unclear
  • Identify the data that matters, which means not all data, only the critical data
  • Make data issues transparent, such as data quality & remediation or master data management & replication
  • Embedding of data quality management in daily operations for data that matters
  • Drive fit for purpose improvements

Lastly, there needs to be a focus on upskilling the employees on data skills.

 

Are there challenges for the adoption of data technology skills and culture? How has the pandemic affected these challenges?

 

There are many challenges and plans that have been impacted, but I prefer to look at the opportunities.

I am heading our European Data & Analytics community and normally we hold local and focused events on specific data topics. These can be lunch & learn or deep dives on how to start on the AI journey, sharing best practices from a recent analytics project, etc. 

Due to the pandemic, we organized virtual sessions. These events offer the opportunity for staff to virtually meet other colleagues outside their daily routines and join with D&A communities in larger events across the organization, such as data literacy programs or DIY boot camps.

At the same time, the pandemic has accelerated the business’ digitalization plans, putting much more emphasis on data enablement. As a result, it has increased the need for Data, Data Strategy, Data Governance, Data Quality, Data Skills, and Data Capabilities in the organization.

 

People At The Core of Data and Digitalization

The explosion of available data has given corporations the potential to fuel a new era of fact-based innovation and new ideas through solid evidence. All this culminates in improving operations, clear strategies, and better ways to satisfy customers. 

Yet for many organizations, a strong data-driven culture still remains elusive with data rarely being the foundation for decision-making.

What makes it hard for corporations to be data-driven?

The answer lies beyond data technology. It is about kickstarting the culture at the very top through leadership that sets expectations and decisions anchored in data. The lack of data awareness is something that Hoppenbrouwer believes is one of the major pitfalls for those in leadership roles.

Not all employees are sufficiently aware of the importance of data for the organization. People have learned to store certain data, but they are not really sure why this is important and what other departments do with it.

As a result, data provided by one department to another is often incomplete or contains errors on more than one. Supplementing or correcting this leads to additional work and additional costs. It’s key for employees to understand the data value chain and the role they play in it.

 

What pitfalls should the CDO be aware of when pursuing a data-driven culture?

 

Culture is made up of people, and changing a culture means you need to get a change going with the people. People don’t change naturally unless there is a reason to do so.

Everybody wants to deliver their digitalization strategy due to the value it enables and this is a key catalyst to improve data culture. 

However, data is a foundational enabler, and data responsibilities were considered an add-on to the day job without recognition for good performance in data-related activity.

Leadership needs to change here and needs to step up, from the top, all the way to the supervisors on the shop floor.

What can the leadership do to make these changes? Some examples include:

  • Publicly speaking about the role and importance of data 
  • Clearly articulate how data enables the business strategy and the value it unlocks
  • Data roles & responsibilities need to be formalized AND effort recognized
  • Visibly recognizing the good work done in the organization to get the data right
  • Sharing of success stories and lessons learned
  • Encouraging staff to become more data literate, through sponsoring data literacy events, citizen data science boot camps, and inclusion in personal development plans

Ultimately, the CDO plays a key role here in supporting the leadership to drive change through the organization.

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Digital Healthcare: How Is Technology Transforming Health?

In a post-pandemic world, it’s clear that the need for digital transformation in healthcare has become important. A survey done by McKinsey with 213 European physicians claims that more than 50 percent believe that telemedicine will be a significant part of a modern healthcare system. 

With the digital healthcare revolution underway in European nations, it’s evident that hospitals and clinics will need to adapt to emerging technologies and integrate digital health solutions as part of their strategy. 

But what is digital healthcare? And how is technology transforming healthcare?

In this article, we take a look at some of how hospitals and clinics are integrating health digital solutions, how digitalization is used to fill the healthcare skill gap, and why data will be a significant tool in the care industry.

 

Adoption of Digital Technologies In Hospitals

Europe has been facing unprecedented pressure within the healthcare system and the pandemic has shown that despite the improvement in quantity and quality of care, there is still a gap in efforts toward digitalization.

This is in part, due to several challenges such as bureaucracy in healthcare and the costs of technology for organizations to implement digital technologies.

However, it does not mean that digital technologies are not being experimented with and utilized at all. In fact, due to the pandemic, certain countries are accelerating their adoption of digital and telemedicine solutions to help improve the quality of healthcare services provided.

One such example is Portugal’s use of the ePatient system for centralized and real-time patient data management. ePatient allowed clinicians to monitor and communicate with their patients remotely if they were not able to be present. 

 
 

This adoption of a digital healthcare solution has made home care easier for healthcare professionals in Portugal as they can communicate with each other over the application.

 

The Skill Gap In Digital Healthcare

With digital systems in place, hospitals and medical professionals will need to learn how to fully utilize these solutions to deliver care. However, many in the workforce, especially in the nurses’ field, are still lacking the skills and proficiency to handle digital healthcare solutions and technology.

Before organizations can scale up these digital systems, the digital divide and skill gap within the workforce need to be acknowledged. The workforce that delivers care to patients needs training and support to use new systems, and to use these technologies effectively to deliver high-quality digital care. 

How should organizations overcome this skill gap challenge?

Everything starts at the top and clear strategic directions from those in leadership roles to integrate and train the workforce to embrace new technologies and skills must be the priority. It’s important, however, that when investing in new systems, it needs to be guided by the organization’s long-term visions and account for sustainability.

Beyond that, investing and supporting educational initiatives that provide a platform for the workforce to develop these skills will be essential in filling the medical skill gap. One such initiative is the NURSEED program by a Danish collective company that seeks to address the nursing shortage and skill gap in Denmark through a digital platform.

 
 

Future of Health Is Digital and Data

Putting digital solutions in place and equipping the workforce with the necessary skills will lay the foundation for a digital healthcare revolution for many organizations. The next step is to fully embrace the healthcare digital transformation and understand the role of data analytics.

In recent years, big data tools have played significant roles in healthcare decision-making. This is in part due to the pandemic, which resulted in an enormous surge of health data being available, allowing for bigger and better analytics.

How can the health industry utilize these data?

Through descriptive, predictive, and prescriptive analytics, healthcare providers will have immediate access to necessary information, and improve overall efficiency. 

 

For healthcare professionals, this would mean improved predictive modeling that can alert them of potential risks of chronic illness or even self-harm. And on a larger scale, it can even predict outbreaks.

With predictive and prescriptive analytics, organizations can expect a reduction in overall healthcare costs by reducing appointment no-shows, preventing equipment breakdowns, decreasing fraud, and even managing supply chain costs.

Bottom line: better data leads to better healthcare.

 

Technology Is Transforming Healthcare

There is no doubt that the healthcare digital revolution is underway and technology will transform the solutions and approaches in modern care. The question is now whether organizations are changing fast enough to keep up with the demands of modern healthcare.

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Understanding The Tech Challenges of Retail Giants

With more and more people embracing digital and smart shopping experiences, the retail market is scrambling to adopt new retail technology to remain viable and sustain growth in a rapidly changing landscape.

In this article, we’ll highlight some of the major challenges businesses are facing and the solutions they are looking for. For a more in-depth look at the trends of the retail industry, head over to our Retail Investment 2021 report.

 

Challenge 1: Evolving and Enhancing CX

 

Customer experience is expected to shift even more in 2021 and as consumers become more conscious of their spending, retailers will need to optimize every step of the customer journey to maintain loyalty, and spark growth.

Improving customer journey optimization will involve significant investments in retail technology trends, a key touchpoint of which will be the tech that improves process efficiency such as AI, automation, and customer touchpoints (as well as mapping them out).

A quick view at the core focuses among retail leaders shows that many organizations are prioritizing smart solutions and digital competency to handle customer needs and ensure quality CX.

 
 

What Are They Looking For

 

Improving the experience for customers by delivering fast and accurate responses through CX software which integrates marketing automation, customer service, CRM, CPQ, sales force automation (SFA) solutions, and customer data platform (CDP)

 

Challenge 2: Deciphering The Data

 

Achieving effective customer journey optimization will require targeted investments in retail technology and a high priority tech among retail leaders is data and analytics.

With the influx of data available due to rapid digital transformation, organizations are scrambling to adopt big data and real-time data analytics to better refine their business actions according to customers’ needs and profiles.

As the global big data market is forecasted to be worth $103 billion by 2027, data analytics is no longer just a buzzword, but an important retail technology investment needed for day-to-day efficiency in organizations and individuals.

Given the current talent gap, however, businesses will still look to third-party solutions in terms of building an infrastructure that allows them to utilize data analytics effectively.

 

What Are They Looking For

 

Platforms that implement easy-to-use analytics, data mining, and automated forecasting. Department-specific data such as marketing, sales, and customer analytics will also be a key factor for many businesses.

 

Challenge 3: Digitalizing Stores and Scaling e-Commerce

 

A shift towards improved digital storefront experiences is in line with customer market behavior as globally, 49% of the population is shopping online more now compared to pre-COVID times.

Nevertheless, customers still prefer shopping on-location, with a recent survey done by Shekel showing that 87% of customers prefer to shop in stores, but with touchless or seamless self-checkouts.

As such, improving the infrastructure for businesses’ e-commerce platforms and brick-and-mortar stores has become a race. Those who are able to achieve seamless online shopping experiences and frictionless smart payments will get the lion’s share of the market.

 

What Are They Looking For

 

The ability to transition from an analog business model to a digital, omnichannel model through cloud solutions or optimizing current digital channels such as mobile apps, IoT, and smart shopping.

 

Challenge 4: Improving Digital Security

 

Machine learning and cloud computing continue to be high priorities in tech adoption for retail leaders. Cybersecurity, however, has seen a significant rise due to demands for safer and more secure digital/smart shopping.

The confusion caused by the coronavirus and the massive shift towards digital/remote working has led to cyberattacks becoming frequent with large data breaches increased by 273% in the first quarter of 2020.

 
 

Retailers will face an uphill battle in the “new normal” of post-COVID to assimilate all the necessary digital security strategies, be it upgrading vulnerable software and hardware components or strengthening customer data protection, to ensure customer confidence and loyalty.

However, with the global market for cyber security software expected to grow to $230 billion in 2021, they can expect exponential growth in the practices and solutions for digital security.

 

What Are They Looking For

 

A simplified platform that allows them to reduce security risk through robust privileged access management (PAM) and optimal solutions for customer data storage and protection that comply with GDPR.

 

Overcoming The Challenges

 

At the start of 2021, it’s clear that retail giants are making big investments when it comes to innovative retail technologies. Certain technologies, such as digital transformations, continue to be a major priority for retailers.

The big changes, however, come from renewed interest in improving customer journeys through data analytics and scaling up digital channels via e-commerce or smart shopping experiences.

For any organization, it’s essential to identify which areas of retail technology they are trailing behind, then network with the right solution provider, invest in skilled talents and have the necessary tools to maintain growth in a soon-to-be revitalized industry.

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How to Fully Utilize Data for Improved Customer Experience

Every great business recognizes the importance of customer experience (CX) – a critical strategy in engaging and retaining customers to your brand.

With the e-commerce landscape booming amidst impacts from COVID-19, it’s apparent that CX has transcended through both digital and physical sales channels, and is a key competitive differentiator for brands.

But with the extensive research and analyses on achieving great customer experience, why is CX still an ongoing concern for businesses?

 

THE CX CHALLENGE

 

However, as straightforward as it may sound, it’s becoming harder for companies to achieve the customer experience that consumers expect due to:

 
 

Customer touchpoints are especially significant as these are the areas in the customer journey where the consumer interacts with your brand, and have a direct impact on their overall experience.

 
 

According to customer service provider, Help Scout, “a poor experience at one touchpoint can easily degrade the customer’s perception of multiple positive historical experiences at other touchpoints.” And Qiigo claims that it can take between 13 to 20 touchpoints, or touches, to convert a prospect into a customer. 

Fortunately, as businesses become more digitized, it’s much easier to identify customer behavior patterns and to improve touchpoints in their journey.

However, the amount of raw data available combined with the challenge of analyzing and acting on customer insights are factors as to why organizations are still lacking in quality customer experience.

 

PREDICTIVE ANALYTICS IN CX

 

Unlike prior generations, the consumers of today have higher expectations and a clear idea of what they want and how they want companies to deliver it to them.

But 71% of consumers are still receiving “An offer that clearly shows they do not know who I am” while 41% are seeing “Mistakes made about basic information about me.”

Such errors are taken as signs that the brands are ‘intentionally’ not placing importance on their customers when actually, it shows that organizations are not using their customer data to the fullest potential.

 

Pre-Purchase, Purchase and Post-Purchase

 

By leveraging data and artificial intelligence (AI), companies can improve all stages of their CX journey.

One example given by Capgemini showed how Amazon used AI and predictive analytics, before the browsing prospects even made a purchase, to:

 
 

Qymatix Solutions also emphasized the cruciality of using predictive analytics in the pre-purchase and purchase stages through predictive lead scoring while utilizing churn and cross–selling predictions in the post-purchase phase.

Micro-Segmentation and Personalization

 

In the past, segmentation was sufficient to deliver an ‘adequately personalized customer experience’, but today, brands need to micro-segment their potential consumers for hyper-personalization.

Using machine learning, predictive modeling and data mining, predictive analytics help to:

 
 

In a use case by Wavicle Data Solutions, a restaurant chain’s consumers were segmented into multiple groups and clusters based on gathered data. Following that, “predictive analytics and machine learning created both macro and micro-segments of customers, with matching customized offers for each audience”.

At the end of their process, the restaurant chain was able to develop personalization and loyalty programs that engage customers with more customized offers and meaningful messages, increase customer retention, and grow revenue.

 

Resource Efficiency For Higher CX

 

Aside from giving consumers exactly what they need, predictive analytics also help in the efficient allocation of your resources. 

For instance, a coffee shop saved 38% of their marketing costs by predicting which of their customers were more likely to churn and sending them targeted offers to convert them into loyal customers.

Other examples, given by MarTech Series, show how predictive analytics can reduce resource wastage and streamline costs by planning staffing levels in advance for smoother and more timely customer experience, and upgrade delivery timelines by conveying transport route adjustments for on-time deliveries.

These efficiency strategies not only lead to savings for the company, but also ultimately improve the interactions and experience of the consumers.

But predictive personalization cannot be made without quality data, and data strategy is where some organizations face roadblocks.

 

MAPPING ORGANIZATIONAL DATA JOURNEY

 

While businesses often map out their customer journey, companies should also map out their internal data journey, which can involve multiple functions and C-suites, to determine weak areas in the sharing of their CX data.

For instance, are there information silos between the business departments? Which function has decision authority over data?

In a CX team proposed by TechTarget, the Chief Customer Officer (CCO) is responsible for the customer experience metrics and research while the Chief Experience Officer (CXO) “creates customer journey maps that use data to predict future consumer actions”.

On the other hand, Dion Hinchcliffe, Vice President and Principal Analyst at Constellation Research and Brian Hopkins, Vice President and Principal Analyst at Forrester Research, both talked about data-sharing and partnerships between different C-suites.

Hinchcliffe mentioned that the Chief Information Officer (CIO) and Chief Marketing Officer (CMO) each have a vital part to play in delivering quality customer experience.

Meanwhile, Hopkins believes that the Chief Data Officer (CDO) and CIO can form a powerful partnership to drive data strategy, where IT supports the CDO to maximize the impact of customer data.

To quote Hopkins, “The bottom line is that control over data is neither a pure tech decision nor a pure data decision.”

With more specialized C-level roles and functions emerging, organizations need to tear down data silos and establish active communication between all business functions for a joint effort towards better customer experience.

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CIO Investments: Which Tech Is Your Priority?

As the world crosses into 2021, the distribution of the COVID-19 vaccine has brought surges in global stocks and market optimism.

However, even with great hopes of economic recovery by the end of 2021, organizations still need to ensure that their business growth and plans continue positively. Chief Information Officers (CIOs) are playing a big part in achieving these goals by maximizing information technology (IT) investments and advancements.

 

What IT Investments To Focus On?

 

According to our Executive Trend Survey, 67% of CIOs placed data science as a top priority for 2021 with core focuses on analytics strategy, data management, and big data analytics. 

Meanwhile, cyber security and cloud were named as other top CIO priorities by 59% and 53% of surveyed leaders respectively.

 
 

But what does this mean for CIOs across the industries?

Based on feedback from CIOs and key IT executives, the majority (47%) of them are facing 2021 with slight changes in their goals and a lower budget for their function.

 
 

With limited budgets, CIOs need to pick and choose which goal takes priority over the others and select a solution that will truly give them the return on investment they seek.

Thus, even if CIO trends point towards analytics if their current end objectives don’t correspond with the need for data solutions, they should focus on more pressing investments.

Another key factor influencing their investment priorities lies in the current maturity levels of their technology and operations. For instance, some are still new in forming data strategies while others are more advanced in their data-driven processes, thus their focus areas in the use of data science differ greatly.

 

Investing In Data Science

 

Today, it’s uncommon to find any company that is not taking advantage of their data. From enhancing customer experience to improving predictive maintenance, business leaders are aware that data is critical to their organizational growth.

But which area of data analytics should your organization focus on? Between the different analytics applications and components, what should be the foremost priority?

In recent interviews with CIOs and other IT decision-makers, over 450 of them named analytics as their core focus. Even so, under the analytics umbrella, their interests ranged from big data analytics and predictive analytics to data warehousing and analytics strategy.

 
 

55% of them selected data management as their foremost investment in analytics, naming master data management (MDM) and product information management (PIM) implementation as some of their projects.

 
 

The MDM solution is largely adopted by the banking, financial services and insurance (BFSI) sector to manage massive amounts of transactional data on their customers. PIM, on the other hand, is seeing higher demand by the e-commerce industry and an anticipated fast growth in the media and entertainment sector.

In regards to data analytics strategy, some of the CIOs are investigating how they can make the business work more efficiently through analytics strategy while others are taking the next steps to improve data quality.

On the other hand, a number of the interviewed decision-makers are still setting up and realizing their data strategy, indicating that they’re still in the planning stages and concentrating on becoming a data-driven organization.

 

Investing in Cyber Security

 

Meanwhile, our most recent interviews with CIOs on cybersecurity investments discovered that cloud security is foremost on their priority list followed closely by cyber security strategy.

 
 

From our findings, a number of the interviewed decision-makers expressed interest in implementing security information and event management (SIEM) solutions.

 
 

Another hot spot in 2021 cyber security spending, according to Forbes, is identity and access management (IAM), which is a prime focus for 30% of business leaders investing in cyber security. Some of their projects regarding access and identity management include:

 
 

With uncertainties still forthcoming, some CIOs are worried about guaranteeing a high level of cyber security with a limited budget while facing challenges in approaching the topic of online security to a diversified and remote workforce.

 

Investing in Cloud

 

Based on CIO investment feedback from the interviews, most of them are still in the planning stage of their cloud strategy with cloud integration and migration as their core priorities.

 
 

Microsoft Azure, Amazon Web Services, and Google Cloud are three of the most popular cloud platforms in the market, and interviewed decision-makers are contemplating between the cloud computing services while some are even working with all three of the platforms.

Alternatively, a group of IT leaders and other key C-suites are working towards a hybrid cloud environment, which is commonly used in industries such as:

What is Your Focus Area?

 

As seen in our survey findings and interviews, each of the IT leaders is prioritizing a specific solution that best serves their target goals with consideration to their budget, their available expertise and IT talents, and current processes.

For some, the immediate focus is on surviving the consequences of the pandemic, “which has become the number one objective for most emerging technology investments”, according to KPMG’s research. For others, it’s an opportune time to shift to a more digital business model and accelerate their digital transformation.

Nevertheless, while benchmarking and taking note of emerging IT trends help your organization to measure business performance against other companies, the global situation and market uncertainty are still expected to significantly affect information technology investments.

The important thing is to have a solid focus on your strategic IT priorities, adopting agility and adaptability for business continuity, and making smart investments to prevail in the long term.

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IT Benchmark 2021: Where Do You Stand Among the CIOs?

CIO IT Benchmarking

The outbreak, evolving workscape, a volatile market, changing customer demands –  Chief Information Officers (CIOs) have their hands full in strategizing IT projects while maximizing the value of technology investments.

With the modern CIO role entailing more strategic decision-making, you need to identify key technologies that not only help advance the organization’s digital transformation, but that also increase its business value and competitive edge.

So how effective is your IT strategy compared to other organizations in your industry? Are there areas where your peers are ahead of you? Let’s dive into the IT benchmark data for the coming year.

Data Science Takes The Lead

In 2019, Management Events’ Executive Trend Survey found that 88% of CIOs across Europe were focusing on cyber security adoption for the coming years, followed by cloud and big data.

However, the pandemic has shuffled tech priorities with the latest survey discovering that data science and analytics have taken precedence over cyber security for 2021.

Although the findings point towards a higher importance of data science, the surveyed CIOs seem to be almost equally torn between data, cyber security and cloud investments. The close gaps indicate that these three technologies are vital parts of CIOs’ business continuity and recovery strategies.


The Rise Of Data

Based on our survey and interviews with IT decision makers, their core data focuses are on analytics strategy, data management and big data analytics, with the majority looking for data science and analytics to be scaling within their organization in 3 years.

Almost 82% of the surveyed leaders said they are updating data analytics models to accommodate changing market behavior. Furthermore, 73% agreed that they’re heavily investing in data-driven business models for post-COVID-19 survival.


Compared to the 2020 annual budget of between €100,000 and €250,000, the budget allocated by the CIOs for 2021 data analytics spending in their organization is higher, with the majority looking to spend in the €500,000 range.


As for data tech adoption, the majority of CIOs are currently building capabilities by hiring new talents with the required skills. However, in the future, they are looking to change their adoption strategy by investing in other companies to acquire the skills.

Diving deeper into the benchmark data, most of the IT leaders focusing on data analytics are from the retail and consumer industry, followed by the banking and financial services industry. Incidentally, aside from data analytics, the retail CIOs are also looking into e-commerce implementations.


Join the discussion on the latest IT trends with leading CIOs, CTOs and more at 600Minutes Executive IT  in Sweden, Austria, The Netherlands, Switzerland, Belgium, Denmark, Germany, and Norway.

Cyber Security Is Still A Priority

With cybercrimes on the rise, businesses around the world are working hard to prevent data breaches and system disruptions. At the same time, companies are in the midst of strengthening their security framework, from securing endpoints to stronger online protection.

So what are the cyber security benchmark and CIO cyber security focus areas for 2021?

In terms of security investments, data security and privacy is one of the core focal points among European CIOs along with a more robust cyber security strategy and emphasis on cloud security.


As COVID-19 saw increasing security breaches, it’s not a surprise that employee awareness training on cyber security is a top priority for 95% of IT leaders. Meanwhile,  89% of the IT C-executives are expecting a rise in predictive and behavioral detection to prevent cyber attacks.

Similar to data science, cyber security’s annual budget was also ranging between €100,000 and €250,000 for 2020, but has increased to €500,000 for 2021, according to the IT leaders in our survey.

Currently, 58% of the decision makers are partnering with vendors and consultancies for their cyber security solutions, but in the future, most of them are looking at partnering with organizations in other industries to create security ecosystems.

Incidentally, IT leaders from the manufacturing industry make up the majority of surveyed CIOs looking to invest in cyber security.

Cloud Increase On The Horizon

 

On cloud benchmark data, our survey found that cloud is the third topmost tech priority for 2021 with the majority interested in cloud migration coming from the banking and financial services industry.

88% of the cloud-focused respondents are currently looking to migrate their workload to the cloud for increased business efficiency, and the survey also discovered that cloud infrastructure and cloud platforms are primary aspects of the CIO cloud strategy.


Unlike the other aforementioned technologies, cloud migration seems to have different budget ranges. The CIO respondents are divided between spending less than €500,000 and between €500,000 and €1 million in the coming year, when in 2020, the annual budget for cloud was mostly less than €50,000 and between €100,000 and €250,000.

Currently, their cloud adoption approach is partnering with external vendors and training their employees, but 68% of the IT leaders are hoping to acquire the necessary cloud capabilities by investing in other companies in the near future.


How Should You Respond?

Before undertaking a benchmarking opportunity, there is much to consider:

  • Are you looking at industry benchmark data or more towards IT budget benchmarks?
  • What performance or process gaps are you seeking to enhance?
  • Do you have a clear objective for the tech implementation?

While the trends are pointing mainly towards data analytics, investing in this solution must be in line with your organizational and industry goals. As Datafloq puts it, companies need to “dig down to understand if [data analytics] is worth it”, and if it’ll bring them the return of investment (ROI) that they are looking for.

Data analytics use case of a CIO interviewee from a Dutch online travel agency:

  • What’s the objective of analyzing the data? To improve booking experience and behavior recognition
  • What data are they looking at? Online customer behavior
  • What are they using to process the data? Big data analytics and predictive analytics

The same goes for all innovations that are the focus of CIOs. Although market trends are pointing to a certain technology, it doesn’t mean that everyone must jump on the bandwagon.

Data benchmark is just one indicator of your organization’s performance that will potentially inform you on which areas you need to improve, but identifying the right elements to benchmark is the key. It’s vital to choose elements and technologies that will bring the most positive impact to your organization’s growth and revenue.

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How To Become a Data-Driven Organization

1. Which capabilities do organizations need to become data-driven organizations?

The data-driven organization is not a new concept. Put simply, any business that is making business decisions based on facts, rather than based on gut feelings, opinions, and emotions, is a data-driven company. In a data-driven organization not only senior management makes data-driven decisions, but all decisions at all levels are made based on facts. It is therefore about strategic decisions: “are we extending our services to another industry?”, Tactical decisions: “are we hiring this applicant?”, and operational decisions in the workplace: “are we giving this customer a discount?”

Data-driven organizations make sound decisions in a continuous data-driven business cycle. This cycle requires the following three capabilities:

  1. Tech-savvy (Data creation & integration): Ability to create and collect all relevant digital data, and integrate and structure this data into information.
  2. Data fluency (BI & Analytics): Ability to deduce intelligence & insights from data & information.
  3. Data literacy (Decision management): Ability to make decisions & formulate actions based upon intelligence & insights.

HotItem_Data_Driven_Organization

Figure 1: data-driven business cycle with the required capabilities.

Most organizations face difficulties in meeting the technical and organizational requirements to become a data-driven entity. Gartner forecasted that 80% of companies would address their lack of proficiency in data literacy by 2020. Many organizations now recognize this and are starting to change their perspective towards data and analytics. They are beginning to understand that data and analytics can be a significant factor in creating value and shaping business strategies for data-driven businesses. One example is H&M Group’s data mesh journey, a domain-based approach to setting up data architecture within the company.

Tech-savvy capability

Without a big data & analytics platform, the organization is literally driving with blindfolds. Yet qualified people with expertise on the cloud, big data, and data science are scarce and hard to get. And it’s even harder to keep them because a high salary and job security are not enough to keep them satisfied. And even if they are staying with you, they need constant adaptation and learning.

Data fluency capability

Like being fluent in a language, data fluency enables people to express ideas about data in a shared language. In a business context, data fluency connects employees across roles through a set of standards, processes, tools, and terms. Data fluent employees can turn piles of big data into actionable insights because they understand how to interpret it, know the data that is and isn’t available, as well as how to use it appropriately.

Data literacy capability

Data literacy is the ability to read, work with, analyze, and argue with data. Much like literacy as a general concept, data literacy focuses on the competencies involved in working with data.

Every employee on all levels needs technical skills. But being tech-savvy is not enough, soft skills are far more important. Two kinds of soft skills, in particular, are essential:

  1. critical thinking skills: agility, collaboration, creativity, and problem-solving
  2. business skills: communication, negotiation, leadership, project management, planning, delegation, time management, privacy, and ethics.

But the most crucial success factor is the right mindset: Have an open-minded growth mindset (instead of a fixed mindset). Every employee must be accountable for his own success and learning journey. By far the biggest challenge and learning curve is for senior management. Data-driven businesses increase transparency, and transparency reduces power. If that isn’t threatening enough, the rise of Artificial intelligent driven automated decision making is potentially degrading managers from drivers behind the wheel to guiding passengers.

2. Strategic roadmap towards a data-driven enterprise

All three capabilities must be developed and maintained guided by an overarching strategic roadmap towards a data-driven culture. Building a data-driven enterprise is not just about encouraging the use of data in decision-making. Data and analytics leaders must lead the development of the correct competencies and rebalance work to be consistent with their enterprise’s ambitions for generating information value.

A common mistake that organizations make trying to develop a data analytics capability is to hire brilliant data engineers and scientists, let them experiment, and hope for the best. This will surely not lead to analytic solutions that are embedded in the organization and deliver sustainable business value. Don’t treat data and analytics as supportive and secondary to your business initiatives.

First, develop an Enterprise architecture, and let that be the blueprint for further development of the existing data analytics platform. This approach will ensure that the business strategy is aligned with the technical capabilities and actionable insights lead to actions that improve strategic objectives.

Digital transformation is a human transformation: it is not a technological program but a strategic roadmap towards a data-driven culture. Therefore you’ll need an Integral Data-Driven educational and onboarding program’ that is measurable, personalized, affordable and rapidly scalable. Bear in mind that talent is always the constraining factor. There are three crucial factors for every person to make a successful data-driven learning journey:

1) Ambition: The desire and will to change
2) Ability: the skills and knowledge to learn
3) Allowed: the perception that change is supported and permitted

Figure 2: Strategic roadmap towards a data-driven culture

The Strategic roadmap towards a data-driven enterprise consists of two phases:

  1. ROADMAP PHASE
  2. CHANGE PROGRAM PHASE

ROADMAP PHASE

Start with an organizational assessment that analyses the drivers and impacts of the transformation on the organization, assesses the preparedness of the organizational entities to adopt the transformation, and assess the “people and organizational” risks associated with the transformation. Align the business strategy with an integrated data-driven transformation strategy.

CHANGE PROGRAM PHASE

The change program consists of five iterative steps:

  1. CHANGE PLAN
  2. AWARENESS
  3. EDUCATION
  4. LEARNING & EMBEDDING INTO ORGANISATION
  5. PEOPLE ANALYTICS & TRANSITION MONITORING

CHANGE PROGRAM

Develop an integral change program that is optimally tailored to the employee’s level of knowledge and business situation. Use the concept of ‘Education as a Service (EAAS)’ as a framework. Customize and personalize training courses, where possible and needed. Sometimes online learning works best, in other cases team learning is more effective.

AWARENESS

Creating awareness through storytelling and learning journeys. Active commitment and communication of higher management is a key success factor.

LEARNING & EMBEDDING IN ORGANISATIONAL CULTURE

Cultural reinforcement is created by training on the job, apply what’s learned in practice and a continuous feedback loop. Coaching should focus on the three personal success factors: Ambition, Ability and Allowed.

PEOPLE ANALYTICS & TRANSITION MONITORING

Control the learning transition by making the transition data-driven. Develop a BI & Analytics system to monitor the personal learning journey of every employee, as well as monitoring the crucial transition drivers.

CONCLUSIONS

The journey to become and thrive as a data-driven organization is a data-driven human transformation. This transformation is linked with business vision and strategy. Manage the cultural transition with an integral data-driven educational & onboarding program. Monitor the learning journey with people analytics. Focus on the sustainable learning of technical as well as soft skills. Allow room for experiments. Start today. Learning is fun!