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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.

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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!

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Process Optimization & Automation With R&G Global Consultants

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The technological advancement that is Robotic Process Automation (RPA) first arrived on the scene around 5 years ago to a gargantuan storm of discussion about its disruptive potential for Process Performance. Even today the arguments continue, with unscrupulous license sellers on one side promising a digital revolution, and a scaremongering media on the other, who claim RPA is a disastrous threat to jobs.

Caught in the middle, are the businesses that desperately want to improve their Process Performance. Not to mention a global economy desperately in need of technological advancements which will help improve wealth and living standards for all.

The hype was huge. But as many have now found to their cost, it was akin to being fleeced by a backstreet bookie. Now that whistle’s been blown, it’s time for everyone to get a grip on reality when it comes to RPA.

Placing the big bets……

High-pressure sales tactics for RPA licenses have lured many an unsuspecting CEO and digital leader into believing RPA is a fast track to competitive advantage, the key to unlocking unprecedented levels of productivity. But the reality experienced by many organizations has been the polar opposite, with Process Performance crashing spectacularly amid the disarray of another failed deployment. Meanwhile, a great deal of process insight, expertise and management has been thrown on the scrapheap in favor of RPA licenses. Talk about collateral damage.

You couldn’t make this up: “We did RPA – the Business Case just did not deliver”, “We believed the hype – now our business process is a mess. Can you help?” and “We took out headcount and killed our service performance” are comments I hear all the time. Just last week I was told, “Our RPA programme didn’t deliver the business case, we need to take another 10% out of our headcount”.

For balance, I have also heard “Yep, it worked for us – it was a real game-changer.” But certainly less often. And for clarity, R&G was in no way involved in any of these RPA deployments.

The fact is “only 3% of progressive leaders have reached any form of scale with more than 50 robots in service” (Deloitte, 3rd Annual Global RPA Survey). And despite being willed on by the masses, RPA has so far been a dismal failure in answering the world’s productivity challenges. A ‘reset’ in thinking is required.”

Bill Gates was on the money when he said……“The first rule of any technology used in a business is that automation applied to an efficient operation will magnify the efficiency. The second is that automation applied to an inefficient operation will magnify the inefficiency.”

Given the number of organizations still contravening Bill’s second rule, it appears a lot of people either weren’t paying attention or just didn’t believe him.

Game of chance

If we look at a typical RPA deployment it generally consists of an RPA Assessment, a business case (that involves people and their jobs) and a quickly derived implementation plan. But with little to no consideration given to upstream or downstream dependencies. No end-to-end assessment of business performance along the entire value chain, nor consideration of the overall impact on the internal employee experience. Customers are not engaged in the end solution and the organization often remains ignorant of the impact on them. The fact that many organizations aren’t tuned in to their customer experience in the first place – when they absolutely should be – is another matter.

Spot the problem?

In essence, an enormous array of factors are being left to chance. Which leaves all the room in the world for both positives and negatives to be amplified. Especially when organizations caught up in the excitement of their shiny new technology decide to create a “Centre of Excellence” for their RPA which, in the high likelihood of inefficiencies being present, does nothing other than make them truly outstanding at automating these inefficiencies and building waste into the process, while making it even more difficult to pick through the aftermath of an enterprise-wide RPA deployment.

Stacking the odds in your favor

The thing is, it’s not that difficult to make any RPA (or digital) deployment a thing of beauty. But this will only happen if you jump off the RPA hype bandwagon and get back into the real business process world.

  1. Take an end-to-end view of the overall business value chain – your people can help you understand this.
  2. Know the dependencies and interactions that exist throughout your value chain – you already have the data that tells you this.
  3. See your real Process Performance – at a granular level, variance-based, data-driven
  4. Innovate using the array of digital solutions available to you (including RPA) – blend some old school process thinking and human intelligence with process mining, analytics, automation, machine learning, etc.
  5. Validate the positive impact any process automation has on your customers.
  6. Go again.

In his theory of constraints, Eli Goldratt talks about being on the lookout out for the “Herbies” in your process – those activities or steps that inject the largest amount of variation or delay. It stands to reason that any deployment should eliminate variation and inefficiency. Then you have a chance of taking performance to the next level while lowering your RPA investment cost, increasing customer satisfaction and benefitting from your employees being more engaged with their day-to-day work.

Horses for courses – build your Data-Driven Leadership capability

Ultimately, as with every other piece of digital process tooling with the possibility to ‘transform’ your business performance, it all comes down to appropriate use. Using the right tool for the right job. RPA will not be the silver bullet that solves all (or any) of your problems in isolation, but there is no doubt it will continue to help drive improvement.

Meanwhile, applying a Data-Driven Leadership mindset and blending classical process thinking with an ever-growing suite of digital tools will go a lot further to realizing the transformational effect now demanded from any digital implementation.

At R&G, we recommend starting with establishing a business purpose, then bridging into getting the right access to the right data (that you already own) and putting the right structure around it. Our approach then pivots on the ability to do the right process analysis with the right digital tooling. But this is 100% reliant on the people within your business bringing meaning and context to the insights. Strength of leadership is imperative in making and standing behind decisions, then executing the right intervention to deliver the right business outcome.

R&G’s Data-Driven Leadership approach enables you to examine your situation with the end-to-end process always in clear view, fix your inefficiencies and pave the way to enjoying optimal benefit from any improvement initiative (including an RPA deployment). While at the same time, stacking the odds in your favor with some beautiful digital tools.

Why take a gamble when you’ve already got the means to identify a certain winner?

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Därför ska du digitalisera din verksamhet

Tekniken förändras precis som vi människor. Användandet av digitala tjänster har förändrat vårt sätt att driva affärer. Att automatisera och digitalisera din verksamhet är en förändring som kommer leda ditt företag i rätt riktning. Här är 5 anledningar till varför du ska digitalisera din verksamhet.

Sakernas internet (IoT)

Företag har aldrig varit så snabba på att anpassa sig till teknik relaterad till IoT som de är idag. I en undersökning utförd av Gartner Inc kan man se att hela 43 procent av alla bolag skulle använda sig av någon form av IoT relaterad teknik i slutet av 2016. Kanske är du en av de 43 procenten idag? Dock är vår spaning på att knappa 10 procent av bolagen har anpassat sig till sakernas internet. Oavsett om det handlar om 10 procent eller 43 procent är det här en trend att ha koll på. Det är en trend som verkar hålla i sig, i alla fall när man tittar på en rapport publicerad av McKinsey. Där lyfter man fram att IoT år 2025 kan ha ett värde på 10 biljoner dollar. Hur fort ett företag anpassar sig till tekniken är varierande beroende på bransch. Dock ser man en ökning för varje år när det kommer till företags anpassning av teknik.

Minskade driftskostnader

Vi slår ett extra slag för bankindustrin när det kommer till att ta vara på digitaliseringens fördelar. McKinseys rapport från 2016 berättar att driftkostnaderna för banker runt om i världen har minskat tack vare digitaliseringen. Bankerna kan förväntas reducera sina kostnader med 25% med hjälp av digitala metoder som automatisering av back-office, Big Data analyser och molninfrastrukturer. Självklart handlar inte detta enbart om banker utan även om andra branscher. Fler branscher kan snabbt och effektivt minska sina driftskostnader genom att leda vägen fram till att skapa möjligheten för låga kostnader.

Molninfrastruktur

Det handlar om att göra smarta val. Fysiska servrar är snart bara ett minne. Amazon Web Services, Google och Microsoft Azure ger företag möjligheten att välja smart och anpassa tekniken med tiden. Dropbox började på Amazons lagringstjänst S3, det hjälpte företaget att växa fort på kort tid. Företagets grundare menar att utan AWS hade företagets snabba tillväxt aldrig varit möjlig.

Customer relationship management

Idag har det första mötet mellan kund och företag flyttat till en ny arena. Hur kunden tar kontakt med företaget har förändrats. Det är inte längre vanligt att kunden tar kontakt via mail utan den första kundkontakten sker nu genom sociala kanaler som exempelvis Facebook eller Twitter. Det förändrade kundbeteendet har gjort att företag flyttat fokus och följt efter sin kunder till den nya arenan. Samtidigt som företaget får mer kolla på sina kunder minskar dessutom CRM kostnaden, framförallt eftersom allt fler väljer smarta Saas-lösningar som salesforce eller vårt svenska upsales. Ett exempel är American Express som använt salesforce sen år 2010. American Express har med hjälp av salesforce anpassat betalmetoder och utvecklat verksamheten för att kunna möta sina kunder på rätt sätt och på rätt ställen.

Ökad produktivitet

Digitaliseringen har öppnat dörrar för företag som gör det möjligt att nå resultat med färre anställda och mindre kostnader. I alla verksamheter, stora som små, gäller det att hitta de funktioner i verksamheten som går att göra digitala. Var gör du idag manuellt som går att digitalisera? Ett exempel är Siemens, de erbjuder tjänster över ett flertal områden, allt från maskintillverkning till livsmedel. De granskade sin verksamhet för att se vart man kunde hitta digitala lösningar på de tjänster som man då utförde manuellt. Efter att ha tittat närmare på området rörande förpackningar hittade man arbetsprocesser som var lämpliga att digitalisera. Idag har Siemens med hjälp av digitaliseringen och ny teknik ett automatiserat system inom sin verksamhet för förpackning. En bransch som många trodde skulle dö ut.

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Henrik Von Scheel: Knowing What it Takes For Businesses To Excel in Industry 4.0

Henrik von Scheel became a household when he introduced and originated the concept of the “4th Industrial Revolution” (Industry 4.0), in addition to his many works in triggers global themes, national economies practices, reset policies, influences in GDP growth, and shaping the performance of the fastest growing Fortune 100 companies.

Hailed by Financial Times as one of the leading authorities on strategy and competitiveness, von Scheel breaks down the different stages and focuses that organizations today need to be aware of to excel as a business in the fourth industrial revolution.

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The 3 Stages of Adoption To Deliver Business Value

Industry 4.0 has introduced a new age of disruption that’s rendering old methods and systems obsolete. At the same time, it has allowed different industries to carve a path towards innovations, new ways of thinking, and most importantly, new drivers to enhance business value for customers.

While different industries have different drivers, von Scheel believes that in Industry 4.0, there are three common stages of adoption that organizations should integrate into their business strategy to further drive productivity and excel in today’s competitive landscape.

Stage 1: Increasing Operational Excellence

To stay in business, organizations need to focus on Operation Excellence for non-core activities like Human Resources, Finance, and Procurement, to consistently perform and deliver.

Industry 4.0 pushes the development and integration of emerging technologies such as IoT, Artificial Intelligence, Cloud Computing, Advanced Analytics, and more. These technologies will be crucial for increasing productivity, lowering risks, cutting costs, and operational efficiency.

Stage 2: Improving Growth

“The essence of growth is that you are better than your competitors,” notes von Scheel. While most organization’s competitive competencies only constitute about 15% of their strategy, it’s still a key area to improve for organizations to further drive growth and productivity.

The advent of Industry 4.0 has introduced applicable systems such as Smart Supply Chain Management, Smart Products, Integrated Ecosystems (upstream/downstream), Smart Automation, and Smart Contracts (through Blockchains), to further streamline growth for a business.

Stage 3: Increasing Competitiveness

Differentiation will be the “X-Factor” for organizations to excel in an increasingly competitive landscape. For von Scheel, Differentiation is “what you do, every day, through repeatable activities to serve your customers better than the competition.”

The rarity and difficulty of this discipline mean it only constitutes 5% of an organization’s strategy as it can take months to develop and define an organization’s Differentiation. However, with Industry 4.0, early adopters can speed up the process through emerging service products or business models such as Bioinformatics, Nanotechnology, or Quantum Technology, to put themselves at a true competitive advantage.

Different Levels of Productivity Drivers For Manufacturers

While the stages of adoption can apply to different industries at different levels, the same can’t be said when it comes to applying key value drivers that can capture impact scale and streamline productivity in the age of Smart Manufacturing.

Von Scheel points out the different value drivers factors that can define the productivity imperative for manufacturers of different levels and ensure high-level productivity in Industry 4.0:

Small-lot (Focus on optimizing efficiency): Here, the key value drivers are an integrated product data model from engineering to commissioning, digital worker enablement, and data-driven Overall Equipment Effectiveness (OEE) optimization.

Mass-customized production (Focus on certain degrees of product variance): To uphold high output and consistent quality while enabling a certain degree of product variance, organizations need to integrate closed control loops (enabled by sensor-based, in-line quality inspection), flexible routing, scheduling, load balancing and performance management, and the extension of automation to final assembly.

High-volume production (Focus on fully automated production and maximized OEE): Similar to mass-customized production, the key value drivers will still be on closed control loops through sensor-based in-line quality inspections and transitioning the remaining areas of manual labor through automation and traceability.

Scaling Up Business With 3 Key Principles of Industry 4.0

For businesses to scale up efficiently at the age of Industry 4.0, von Scheel highlights an organization’s need for “focusing on value, mobilizing the organization, and innovating the infrastructure.”

To capture value at scale, von Scheel outlines 3 key principles that organizations need to adhere to:

  1. Think value-backward, not technology-forward. Focusing on the key value drivers and establishing a compelling Industry 4.0 vision is crucial.
  2. Be people-centric, not tool-centric. Clear business leadership mindsets and top-management support should be a priority for Industry 4.0 transformations and capability building.
  3. Innovate the infrastructure towards integrated technology stack. Before an organization can scale globally, the infrastructure should enable local operations and establish as many use cases deliver value through on-premise infrastructure.

Establishing and integrating these key principles will help with scaling in the Industry 4.0 era and help build a solid foundation for consistent growth throughout the scale-up period of businesses.

As the 4th Industrial Revolution continues to take place, it’s becoming increasingly important for businesses to be fully prepared to adapt to the needs of the Smart Manufacturing age.

While this article only scratched the surface on what actions businesses should take, Henrik von Scheel will explore deeper insights and strategies for Industry 4.0 and Smart Manufacturing with his workshop: Industry 4.0 Masterclass – Putting The Industry 4.0 Into Practice. Join von Scheel and more at the 2nd Annual Smart Manufacturing 2020, taking place on the 25th – 27th March at Kuala Lumpur, Malaysia.

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Mark Gallagher: Driving The Future Towards High-Performance Through Big Data

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The future of data-driven organizations has arrived and spearheading businesses towards operational excellence is the vision that Mark Gallagher, the founder and CEO of Performance Insights and Industry Analyst at Formula One, continues to advocate.

As organizations start to adopt more data-driven strategies, Gallagher shares with us the challenges and solutions in which Big Data presents, the opportunities in which disruptive technologies can provide in tandem with data and analytics, and the future it holds for businesses and beyond.

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The Challenges and Solutions of Data-Driven High Performance

Big Data and analytics have quickly become the key ingredient that businesses need to integrate to remain as a high-performing and agile organization in today’s modern industry. Nevertheless, there are challenges that businesses have to overcome before being able to transform into a data-driven organization.

One such challenge that Gallagher notes is the need for organizations to understand and find which data is most relevant to unlocking new opportunities and not rely on established systems.

“We may wish to gather data from the areas where we have some understanding,” notes Gallagher. “However, the real opportunity comes from questioning established systems and processes and examing data around the unknowns.”

While finding and utilizing data effectively is still a major challenge for most organizations, Gallagher believes the solution lies in organizations finding the right partners and using the right emerging technology to help improve performances.

“It is vital to work with the right partners to develop systems that can make rapid use of data”, Gallagher points out. “Real-time data encourages and facilitates real-time decision making, and this is where the power of AI kicks in.”

In the world of Formula One, Gallagher found that both the quality and speed of decision-making have improved dramatically with the help of partners and AI to help understand and utilize data. This enabled Gallagher to guarantee much higher levels of quality, reliability, risk management and performance, allowing them to “avoid negative outcomes and guarantee more positive ones.”

Utilizing The Power of Disruptive Technology

On its own, Big Data has proven to be a disruptive technology. However, Gallagher believes that several emerging technologies can be “game-changers” for the traditional business processes.

“The opportunities afforded by AI and Blockchain technology are only just being realized, and far from dehumanizing businesses”. Gallagher continues, “these tools will enable more people and organizations to work together seamlessly to drive improved outcomes for their customers, businesses and supply chains.”

The benefits of emerging technologies go beyond organizational efficiency and Gallagher points out how Internet of Things (IoT) and artificial intelligence have helped build a more connected and data-driven environment in Formula One.

“We operate a fully connected environment so that we can manage our assets remotely, monitoring performance, quality, gathering diagnostic information and ultimate managing the product life cycle better than ever”, Gallagher remarked on the usage of IoT and artificial intelligence platforms.

Gallagher sees the innovation that IoT and artificial intelligence brings to Formula One, providing information to make better use of their resources and dramatically improve their manufacturing systems. “In creating a digital twin of our product, we have moved to an environment where we can manufacture and manage much more efficiently.”

The Big Future of Big Data and Analytics

Focusing on the data that matters should be the priority for organizations, and as vast amounts of data become increasingly available, Gallagher and Formula One needs to work with solutions that cut to the core of the issues and opportunities that are affecting businesses.

Gallagher points out how Big Data and analytics can be utilized in new ways for businesses and society as a whole in the future, noting that in “a data-rich world, we can mine more opportunities to add value.”

Beyond the profit margins, Big Data has the opportunity to develop innovative solutions and Gallagher shares this enthusiasm saying that he is ”very optimistic that many of the problems facing the world today will find their solutions in technology that develops as the result of having the data to understand issues properly.”

At the end of the day, data is just information and when businesses can access a better quality of information, they can expect to improve outcomes across all areas of operations.