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Matthew Bertram: The Biggest Mistakes Companies Make with Digital Initiatives

We were delighted to feature Matthew Bertram, senior consultant at Future Point of View, at a recent session, Digital Transformation: Charting the Course for Sustainable Success vs. A Temporary Fix. In this exclusive Q&A session, Bertram shares expert insights on pressing topics such as digital investments and emerging technologies, as well as advice on how to overcome talent shortages and improve company culture.  

 

The Digital Transformation Journey

 

What are the biggest mistakes companies make when it comes to digital initiatives?

Failure to invest! You need to be taking 1% of your revenue to invest in research. I see so many companies put in 0% because they have so many operational issues. They then get caught by surprise by something that they should have seen coming. It really doesn’t have to be that expensive. You can buy two VR headsets for less than $1,000 and give them to people in your organization. Ask them to take a couple of hours a week to set up a remote VR meeting and see how it goes. This does not have to be rocket science. 

 

How should different forms of automation be considered in a company’s digital journey?

My favorite is robotic process automation, which is what you do when there’s no computer interface or API available. It’s not a technology question but a business question. What metrics do you want to move? Do you want to increase profitability? Do you want to increase time to value? Do you want to reduce the error rate? First, start with the measures that are affecting your business that you want to improve, then move in with process mapping. Work backward to find out what needs changing. Once you know what to change, then it’s simply a matter of what’s the best technology.  

 

Do you think companies will keep investing heavily in digital projects?

I think it will be a mix. There’s this great desire to go back to the way it was before. I think that the further we get away from another black swan event, the greater the draw is for people to maintain the status quo and go back to business as usual. That’s why this is always a great opportunity for any company that’s willing to invest and maintain the discipline of consistent intentional investing. 

 

How do we improve employee engagement around cybersecurity to protect our assets?

One of the trends I foresee is that cyberattacks are going to get worse. I personally believe in Incident Response playbooks. For example, a ransomware attack has just occurred in your organization. What are you going to do? Being able to help them work out not just a checklist, but a playbook of what is going to happen is very useful. Not only does it prepare you when an incident happens, but it also helps with prevention. Playbooks are a great training tool for cybersecurity in your organization. 

 

Emerging Tech: Metaverse, Blockchain, and More 

 

How will the Metaverse affect businesses moving forward?

The Metaverse is three-dimensional, it moves from screens to spaces. We’ve already seen the gaming and entertainment industries move into the Metaverse. Where this is going to work well in business is with near-term opportunities like training simulations. For example, engaging customers throughout the sales cycle by walking them through a building versus just showing them pictures. Also, product design — think of remote engineering teams working on the same components together in three dimensions versus screen to screen. 

 

Is blockchain technology still relevant?

Absolutely. Back in 2018, it was at the peak of the hype cycle but now it’s going through a trough of disillusionment. The big problem is scalability because being able to scale up to real-world numbers of transactions is hard for blockchain as it’s so processor intensive. My projection is that over the next three years, blockchain is going to emerge out of the trough of disillusionment. We’re going to begin seeing industry-wide products that are built on top of blockchain. There are some early ones now with NFTs and cryptocurrencies. But we haven’t seen anything that’s really moved into wide-scale adoption yet. 

 

Are there any other technologies that are up and coming like blockchain?

Absolutely. Augmented reality is taking digital content and putting it on top of the physical world. Augmented reality blends the virtual and the real world together. Over the next several years, you’re going to see growth in this area, especially in enterprise applications, where you’ll be able to interact with objects and get an overlaid human interface. For example, this is going to change the way we interact with banks. 

 

What are you most excited and afraid of when it comes to technology?

Losing freedom of speech. When we moved into Web 2.0 and the social web, there were a handful of companies moderating all the dialogue on the Internet. They can turn people on or off as they wish. History proves to us that we need contrarian views. I’m concerned that private technology companies are going to effectively cause us to lose our freedom of speech. 

 

Talent Shortage and Company Culture 

 

What impact will the hybrid working model have on attracting talent?

I see my clients struggling with this, especially with HR policy. I know one organization with 900 employees where the HR department tried to put in an organization-wide policy on work-from-home unsuccessfully. They’re just trying to be fair, but the reality is that the work-from-home model has to match the particular job function. It is going to be a huge requirement to capture talent because the model you choose determines where you’ll be able to go look for talent, whether locally or around the world. 

 

How can organizations build teamwork and nurture company culture in a work from home environment?

That’s a tough one since every culture within an organization is different. Remote companies need to clearly articulate their branding in writing. It works very well for them to maintain their culture. If there’s a human-to-human connection, one simple thing I do is start weekly staff meetings five minutes earlier to catch up with everyone and see how they’re doing. It’s a great way of maintaining that personal connection. Something else that works well for us is scheduling physical meetups a couple of times a year so we can maintain those relationships. 

 

Do you have any advice on how to work around the current talent shortage?

Whenever it’s practical, buy rather than build, and offload the burden of finding talent to someone else. When it’s not practical, I would invest in tooling and look at how we can reduce the human workload as much as possible. Finally, there’s this idea of IT decentralization, moving a lot of functions that are curling into IT into self-serve out into the organization. It’s been very effective at being able to balance workloads within the organization. 

*The answers have been edited for length and clarity. 

 
Future Point of View is a digital consulting firm based in Oklahoma City, OK. For additional information about the firm contact at info@fpov.com or go to FPOV.com

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Insurance Fraud Detection Using Machine Learning: What You Should Know

Fraudulent insurance claims cost insurance companies and consumers in Europe €13bn annually. Insurance fraud is rife, especially in the property, automotive, and healthcare sectors. Insurance companies are recognizing the need to adopt digital innovations urgently to reduce instances of fraudulent claims and better prepare for future threats. According to a report by Forrester, global investments in Insurtech exceeded $15B in 2021. 

How can AI and machine learning help your organization detect insurance fraud more effectively?

 

How to Detect Insurance Fraud

 

Investigating fraudulent claims is costly and time-consuming for insurers. It is physically impossible for insurance companies to do a thorough check of the thousands of claims that enter their systems daily.   

Early computerized systems could do so much – only allowing rudimentary analysis and search for fraudulent indicators known as red flags. A big limiting factor with this system is that fraudulent claims had to fit into a particular template or else they would not be recognized. Therefore, new technology is a blessing to insurance companies, providing game-changing solutions to enhance and automate processes along the insurance value chain.  

Nordic insurance companies have already modernized their fraud detection processes with RPA, which assists in verifying information located in different sources to detect the right data. Using RPA, an insurance company recorded a decreased claims cycle time from 6 – 10 minutes to 90 seconds. 

That being said, how do insurers ensure the utmost accuracy in filtering out fraudulent claims? This is where machine learning comes in. 

 

Machine Learning to the Rescue  

 

AI is known for simplifying menial tasks and freeing human agents to do more complex analyses. In terms of insurance fraud detection, machine learning applies aspects of AI to give systems the ability to improve from experience with no extra programming by analyzing large, labeled data sets.  

Machine learning can improve fraud detection techniques in the following ways: 

  • Processes data in a short period of time.  
  • Highlights where connections can exist between various factors that human eyes cannot detect. 
  • Applies various data analysis techniques to allow the discovery of new fraud schemes. 

Although it borrows underlying principles found in statistical models, the main focus of machine learning is producing predictions. These predictions are based on the analysis of known outcomes, known as “ground truth.” Machine learning also can search for fraud in unstructured and semi-structured data such as claims notes and documents.  

Furthermore, machine learning can prevent fraud by detecting suspicious patterns in claims processing and customer background checks, which can potentially save insurers a lot of money. Since investing in a fraud prevention system, this Turkish insurer saved $5.7 million and recorded a 210% increase in ROI.  

 

The Insurance Fraud Detection Dataset 

 

The ground truth provides a label that identifies the outcome of each claim based on a historical dataset of insurance claim information and patterns. While there are varying outcomes between insurance claims, the labels are generally divided into “valid” claims or “fraudulent” claims.  

Health Insurance Fraud Detection Dataset 

In this case study, there are close to a million claims records with more than 20 variables. Claims have been assessed and labelled as normal and flagged for possible fraud. Claims that were flagged showed signs of suspicious policy profiles or malicious agencies, claims, or hospital-related fraudulent behavior. A machine learning model was created, a so-called binary classifier, to detect the two labels as accurately as possible. A supervised learning approach was applied since the data was already labelled.  

Auto Insurance Detection Dataset 

This project highlights the challenge of building a model that can detect fraud, where legitimate insurance claims far outweigh the fraudulent ones. This problem is known as imbalanced class classification. The data set consists of 1,000 auto incidents and insurance claims which had a total of 39 variables before any cleaning or feature engineering. Specific types of machine learning models, such as neural networks, natural language processing, and network graph analytics were also utilized in this dataset. 

 

Anomaly Detection in Insurance Fraud

 

Deep anomaly detection is a popular form of machine learning that can be utilized by the insurance industry to detect fraud. In claims processes, anomaly detection will analyze genuine claims by consumers. It then forms a model of what a typical claim looks like which is then applied to larger data sets. Insurers can also use anomaly detection to identify the suspicious behavior of users on an insurer’s network. In addition, deep anomaly detection can be combined with other AI applications such as predictive analysis to further automate the fraud detection process. 

 

Insurance Fraud Detection Using Big Data Analytics  

 

The Digital Insurer recommends a 10-step approach to implement analytics in fraud detection: 

  1. Perform SWOT – A SWOT analysis of existing fraud detection frameworks and processes to identify gaps must be conducted.  
  1. Build a dedicated fraud management team – It is important to have a team, not an individual, handling fraud claims.  
  1. Whether to build or buy – Companies must evaluate whether they have the capacity and resources to build their own analytics framework or whether they need to engage an external vendor. 
  1. Clean data – Remove inefficiencies and redundancies and integrate siloed databases. 
  1. Come up with relevant business rules – Companies should leverage existing domain expertise and experienced resources. 
  1. Come up with pre-determined anomaly prediction thresholds –Companies should provide inputs for threshold values for different anomalies.  
  1. Use predictive modelling – An effective fraud detection method is one that uses data mining tools to build models that produce fraud propensity scores linked to unidentified metrics.  
  1. Use of SNA – Effective identification of fraud activities by modelling relationships between various entities involved in the claim.  
  1. Build an integrated case management system leveraging social media – This allows investigators to capture all key findings that are relevant to an organization including claims data and social media data.  
  1. Forward thinking analytics solutions – Insurers should always be on the hunt for additional sources of data to improve existing fraud detection systems.  

An insurance company’s efficacy in distinguishing between valid and fraudulent claims plays a big part in determining its financial strength, allowing optimal compensation and support for its customers. 

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Impact of the Russia-Ukraine War on the Global Economy: What We Know So Far  

Almost a month has passed since Russia’s unprovoked invasion of Ukraine. Sadly, the war and humanitarian crisis are not over. Governments, private companies, and financial institutions have responded to the war by imposing harsh sanctions against Russia. The domino effect of these sanctions has already begun, taking a huge toll on the global economy. During our session titled The Cost of War: Decoding the Economic Crisis on EU, Ukraine, and Russia, we were fortunate to host Artem Kochnev and Olga Pindyuk, economists from The Vienna Institute for International Economic Studies. 

Both Kochnev and Pindyuk have been doing extensive research on the Ukraine-Russia conflict over the past few years and are subject matter experts on the economic history of Eastern Europe, foreign trade, and financial markets. They give us a clearer picture of the current economic situation, its impact on major sectors, and strategies for leaders to maintain macro-financial stability in an increasingly volatile environment. 

 

Ukraine’s Economy Comes to a Halt 

“Available data shows that more than 50% of the economy has completely stopped operating. This has happened in regions that are currently under direct military attack, as well as in regions with infrastructure destruction,” Pindyuk says. 

The Ukrainian city of Odesa, a major port and transport hub, ceased operations when the war began. Once known as the pearl of the Black Sea, Odesa has transformed into a fortress to prepare for a possible Russian attack. 

Pindyuk refers to the economic situation in Donetsk and Luhansk where the military conflict began in 2014, to assess the possible scale of economic loss of the current war. “In the first two years, the territory which was under attack and ended up not being controlled by the government of Ukraine lost almost 70% of GDP,” she says.  

However, the cost of an economic downturn and destruction of physical infrastructure is nothing compared to the loss of human capital. Pindyuk laments that the biggest loss is the “death, health deterioration, displacement, and worsening of living standards of the vast amount of people currently residing in Ukraine.” 

In terms of economic recovery, Kochnev says it depends on the length of the war. The shorter the war, the quicker the recovery, and vice versa. “The longer the time passes, the higher the chance that the skillful population; people who know how to do business, create products, and organize basic public services, will never come back,” he adds.  

The EU’s Support for Ukrainian Refugees 

Over 3 million people have already fled Ukraine – marking the biggest exodus in Europe since World War II. Poland has welcomed most of the refugees, approximately 2 million people. Refugees have also entered Slovakia, Hungary, Romania, and Moldova through the border checkpoints in Western Ukraine. Fortunately, Ukrainians who fled to the EU are allowed to live, work and study for up to 3 years in EU member countries under the newly enacted temporary protection directive.  

Pindyuk says the EU job market will benefit from the influx of Ukrainian citizens and mitigate the aging population issue in the region. “There is a vast network of Ukrainians who are already residing in the EU. If the refugees are here to stay, the chances of integration into the job market are quite high, especially given the fact that the Ukrainian population is on average, quite well educated,” she adds.  

 

Russians Brace for a Major Recession 

“According to a recent study by the Central Bank of Russia, the country is going to experience a major recession this year,” Kochnev says. This is inevitable due to the combination of sanctions, high interest rates, rising inflation, and weak consumer confidence. “The consumer prices in Russia are skyrocketing by European standards. The median increase expected by the forecasters is about 20%,” Kochnev warns. Panic buying has been widespread as Russians grapple with shortages of imported goods and an impending cost-of-living crisis. Prices of food products such as sugar and bananas have already increased by 15%. 

Kochnev adds that “sanctions first hit the financial markets in the Russian economy, and we have seen a very strong depreciation of the national currency.” The Russian currency has already depreciated more than 100% (200 rubles per US dollar). A whopping Rb2.5tn was withdrawn from the Russian banking system during the first week of the war. Furthermore, many Russians who earn income in foreign currencies have been unable to receive payments since Visa, Mastercard, Western Union, and PayPal revoked their services. 

“The sanctions regime is not going to be uplifted in the near future. I would not expect a quick and robust recovery [for the Russian economy]. It will be a sluggish recovery at best,” Kochnev says.  

 

Significant Effects on the Global Market 

Although Russia and Ukraine are relatively small economies, they account for significant shares of agricultural commodities traded globally, namely wheat and corn. “Ukraine alone exports about 10% of foreign wheat in the world and 16% of all corn. Together with Russia, they account for 30% of global wheat exports. The majority of these exports are geographically concentrated in the Middle East, Southeast Asia, and China,” Pindyuk explains. 

Countries in the EU may feel the pinch of more expensive goods, but Pindyuk points out that less affluent countries may suffer through “increased poverty rates and political risks due to worsening of living standards.” Pindyuk adds that Ukraine and Russia are big players in global metal markets, and the effects can already be seen in prices for many different metals and commodities.  

In addition, the future of energy in Europe hangs in the air with growing restrictions on Russian oil and gas imports. Kochnev expects “an increase in prices of key energy supplies, given the announced plan of the European economy to diversify their energy inputs away from the Russian suppliers.” 

Russia makes up around 40% of the EU’s gas imports. Gas prices in the EU and UK surged at the beginning of the war due to supply shortage worries but seem to have stabilized for now as Russia and Ukraine hold more peace negotiations. Nonetheless, this has not trickled down to consumers as they are still dealing with high energy bills and petrol prices.  

Earlier this month, the EU introduced a plan to remove its dependence on Russian fossil fuels by 2030 by focusing on renewable energy sources and increasing energy efficiency. However, the effects of surging gas prices are already in motion. “Our simulations showed that doubling the gas price would lead to an increase in inflation rates by 3.5%,” Kochnev explains.  

 

Mounting Inflation Rates in the EU

Consumer prices in the Eurozone unexpectedly increased by 0.9% on a monthly basis since the beginning of the year. Economists are predicting inflation will rise above 6% this month due to severe disruptions to the energy and commodity markets. Based on the official forecast by the European Central Bank, EU residents must prepare for an inflation rate of 8.5% by the end of the year. If this happens, Kochnev says it will be the EU’s highest inflation rate in decades.  

Inflation will impact each of the EU’s 19 countries differently. “Poorer countries are going to be hit a little bit stronger, and richer countries probably are going to fare a little bit better,” Kochnev says. Russian regulators and authorities are also keeping a close eye on the financial assets of European companies in the Russian economy. “They account for a significant chunk of the Russian financial market, at least in banking. Russia doesn’t want to lose management competencies to foreign companies. They don’t want to disrupt the consumer patterns in Russia, in addition to what has already happened,” Kochnev adds. 

In terms of trade, Pindyuk says there is no need to panic yet as “the effects are going to be quite small based on our estimates.” Based on her research, there will be a small decline in exports of air transport, mining services, other transport, machinery, and pharmaceuticals.  

What Happens if Russia Surrenders?

Kochnev talks about the effects on investors in the EU if Russia defaults on the war. He reminds us that Russia lacks foreign currency due to ongoing financial sanctions, especially the euro, which is the major currency of Eurobonds issued in the last six years.  

“Russia has a very low likelihood of paying its debt obligations in foreign currency. If you have certain obligations in the Russian government or Russian companies, you will probably have to drop their valuations down to zero. You will have to cut your books and recognize certain losses, and then struggle for several months or years to recover those assets, transforming them from ruble to euro,” he adds.  

On the other hand, Kochnev cautions EU citizens that inflation rates are not likely to normalize this year. “The recovery in the EU after the COVID crisis has not finished yet. It is fair to say that inflation will stabilize in the second half of 2023,” he says.  

 

Navigating the Growing List of Sanctions 

According to Kochnev, compliance executives are working around the clock as sanctions against Russia and supporting regulations are being updated on a daily basis. Unfortunately, these sanctions are “not always very carefully elaborated, at least when it comes to the EU regulations.” 

Kochnev splits the sanctions into five categories to provide a useful framework for compliance departments:  

  • Symbolic — For example, media restrictions. “They are not going to have a very big economic impact; they just make the life of the Russian government a bit more complicated.” 
  • Individual — “Government officials, members of Parliament, and top businessmen, account for the largest number of overall sanctions.” 
  • Finance — “These are banks and operations with the Russian Central Bank and state-owned enterprises. This had a particular impact on the Russian financial markets.” 
  • Export bans — This includes arms, gas and oil equipment, and luxury items. “Gas and oil equipment is very significant because it affects the ability to modernize and explore new gas and oil sites and mining locations.” 
  • Import bans (fuels and metals) — These account for 60% to 70% of Russia’s exports. “So far, fuel restrictions were introduced by the United States and Canada because they do not import as much from Russia. The EU also recently introduced a ban on metals.” 
 

Three Key Risks for Industry Leaders 

If your organization conducts business with Russia, what possible risks might you face? Kochnev breaks down the risks in three areas: 

Compliance

“The sanctions list is being updated at least every day. This will be critical in the areas of banking, business and deposits, and investments in European banks. Check for the secondary effects of sanctions, taking the U.S. as the best-case scenario.” 

Regulatory

“In European jurisdictions, trade has stopped in both Russia and in Ukraine. You will have to follow up on how to conduct new ways of trade and transfer money from one account to another if you have assets in these jurisdictions.” 

“You will have to assess very quickly and carefully. What might asset freezing potentially mean? What are legislators in Russia and Ukraine going to do with imposing restrictions on moving capital and blocking accounts of certain companies?” 

Macro

“Due to rising inflation rates, you will either have to reduce your limits when it comes to trading. If you are part of finance, you will need to start actively hedging. Sitting and not doing anything will probably expose you to huge risks and losses in your trading book. You will need to find certain investments that can compensate the losses.” 

 

While it’s still too early to gauge the full impact of the war on the global economy, the crisis has shown that organizational resilience and agility are more important than ever. Industry leaders must monitor the war closely and proactively make changes to their business when necessary.  

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Joost van der Vlies: What Tech Leaders Should Know About Software-Defined Logistics

Disruptions are commonplace in the supply chain and logistics industries. Thankfully, technologies such as Software-Defined Logistics (SDL) offer game-changing solutions to obstacles in the supply chain. We speak with Joost van der Vlies, CTO and Head of Architecture at PostNL, on the benefits and challenges of implementing SDL, as well as important insights on cloud technology in the logistics space.  

 

SDL is an emerging approach for EDGE computing and PostNL is one of the pioneers in this area. How do you define the use of SDL for enterprises? What benefits does it bring?

SDL is all about using data and algorithms to steer the supply chain in all its aspects, from forecasting, planning, execution, monitoring, communication, and making real-time decisions automatically. For example, our network setup before determined the physical flow of a logistical item (e.g., parcel), now it is the digital twin of that item that determines the physical flow through our third-party networks. The digital twin contains not only the metadata of the parcel and the order but also customer and operator preferences which can be updated in real-time. For example, deciding on the sorting belt to change the operator from home delivery to retail delivery as the consumer updated their delivery preferences, or to change the operator from bicycle delivery to truck delivery as the item was much heavier than communicated. This means SDL is about sense, deciding, and responding, which makes logistics much more flexible and dynamic. Interestingly, this also creates a lot of new data, which can be leveraged in ways not thought of before. 

 

For PostNL, how are you effectively utilizing SDL as part of your cloud strategy? What is the framework and how can CTOs apply it to their organization?

Our cloud strategy is a multi-cloud strategy comprising SaaS, PaaS, and IaaS service providers, and a strong connectivity layer that also includes Edge environments. SDL is part of a more digitized business and cloud is the de facto delivery model for digital business. Within our cloud strategy, the emphasis is on cloud-native component-based application architectures, which can automatically scale depending on the logistical volume and can take part in the sense and response patterns that SDL requires. We train our machine learning models in the cloud and deploy them where decisions are made, that can be both in the cloud or on the edge. As response time and throughput are essential factors, we use global tier 1 internet service providers that provide abundant capacity, maximum uptime, and truly global coverage (for our international business), and private network partners where necessary. 

 

Of course, with any emerging technology, there are challenges and obstacles. Currently, what are the main challenges that tech leaders need to be aware of with SDL?

One of the challenges is that not all existing applications have been designed to operate in a real-time use case, so temporary measures might be necessary as well as a structural re-architecture. Here’s another example — when using machine learning models, it can have a more complex deployment model having an AI platform develop and re-train the model, and have it embedded in an algorithm in or near the business application it is used. And with SDL events can occur from a multitude of actors, which need to be handled in a highly scalable rule engine and using a single source of truth state machine of logistical items. Tech leaders should also be aware of the impact of the business operations on the people working thereof which their work will be impacted. Business and IT should jointly work on SDL and have a change management process from the start. 

 

How did PostNL overcome these pitfalls? What can other CTOs learn from your approach in tackling challenges?

Regarding algorithms, in the past years, we moved from data science hypothesis projects to the development of algorithms with learning models for use in production. That is only possible in a multi-disciplinary approach combining data scientists, data engineers, and the DevOps teams where these algorithms will run or with which it will be integrated. We were not afraid of taking a high-profile initial case and started working on this, learned from it, and eventually earned a computable award in 2020 with an algorithm that predicts when a parcel will really arrive. This is the same for the real-time data case. It requires a multi-disciplinary approach, time, and capacity for innovation, as it has a lot of consequences not all immediately known from the start. 

 

While cloud adoption is gaining momentum, there is still hesitancy among enterprises to fully adopt it. What should the approach be for CTOs to encourage technologies such as cloud and SDL within the organization?

Technology is not an island. Technology supports businesses to become successful. The processes of our customers and our own are getting more and more digital, and increasingly we do business with applications and machines instead of human interaction. Yes, an API is a technical way of accessing data and functionality, but in essence, it is a 100% digitized business service. Together with high volumes, the increasing number of digitized actors in our ecosystem, and the increasing flexibility our customers are asking in the e-commerce domain, cloud and SDL are essential capabilities for digital business. 

 

Finally, what advice can you give to other organizations that are starting to invest in cloud technology? What are the common mistakes that CTOs should avoid when making their transition into the cloud?

Firstly, cloud is not an infrastructure play. It is a full-stack play and includes, or starts with, an application strategy. Rehosting only will not provide true benefits. Understanding business drivers and the requirements for the applications in the future will be input for decisions to buy, consume or make. It also influences decisions to retire, replace, or re-architect those applications, which has direct consequences on the cloud strategy and roadmap. 

Next, skills around networking, storage, and high-performance computing are important and still very relevant when moving to cloud. You should continue having these skills onboard to avoid problems in the long run.  

In addition, the term multi-cloud is used a lot in the industry, though it is much more than using two or more public clouds. For us, any service a partner provides through the Internet is a cloud. This multi-cloud has to be managed from an overall functional, technical, and multi-supplier perspective. Lastly, when starting from a pure on-premises environment, the current IT department setup will probably not be aligned to cloud. Therefore, setting up a cloud competence centre is crucial.  

*The answers have been edited for length and clarity. 

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Swarovski’s Jochen Schmidt: Embracing Digitalization in Retail and the Need of Reliable Data

One of the keys to achieving success in the retail industry is to adopt a data-centric approach to make educated, fact-based decisions for customer satisfaction, better return on investments, and having an overall better retail experience. However, with only 16% of retailers considering themselves experts in data harnessing, it’s clear that retailers are still behind in fully utilizing the power of data. 

As the Vice President of Distribution & Real Estate for Swarovski Corporation AG, Jochen Schmidt shares with us his insights on embracing the digital transformation, the need for clean and reliable data, and why talents are the key enablers for data-driven retailers. 

 

Embracing Digital Transformation and Abandoning Legacy Systems 

The value of digitization has increased significantly in the last few years due to the pandemic with digital transformation efforts for retailers focusing more on improving the overall customer experience, becoming more agile, and providing better collaboration within the supply chain. 

With the global digital transformation market expected to reach 388.51 billion by 2026, Schmidt shares his insights on what challenges await retail leaders that have yet to embrace digital transformation and how they overcome and abandon legacy systems. 

 

What are the challenges for retailers to fully embrace digitalization?

The challenges might be different from organization to organization. Larger retailers with a global footprint have the challenge of overcoming legacy systems, which is a hurdle. Others might face challenges in resources or strategic priority and the speed for implementation of the transformation. That can be a real challenge because the world is evolving so fast and COVID brought an acceleration into it.  

Swarovski operates in more than 100 countries and has legal entities in more than 30 countries. The challenge for us, which goes along with the speed of transformation, is the need to relate with local laws, to adjust processes and systems, depending on the country. It’s not like you just push a button and roll things out on a global scale. There is a complexity which comes with operating in different countries.  

So, think “speed”, whether the systems in place today correlate with each other, and the complexity with different legislation through different countries. 

 

Legacy remains a challenge for retailers. How can retail leaders encourage their organizations to abandon legacy systems?

It’s just a matter of priorities, providing the right funds and the right resources. When given the funds and resources, then every organization can move towards abandoning legacy systems. If there is no investment into IT infrastructure, then there won’t be any movement. 

Essentially, it starts with the willingness to invest and the need to invest is understood by the management. And it is what Swarovski has done in the past. where we released budget to significantly invest into our systems to upscale online and bring systems together. 

This is the starting point. You then bring the different teams, the organization behind it, have change driven by the leaders of every department, create transparency, create understanding, and have one joint vision of where you want to go. So, a good game plan and change management is needed. 

 

Cleaning Up the Data and The Key Enablers for Data Utilization 

Data is the fuel that drives the decision-making process for organizations. In the journey towards becoming a data-driven organization, retail leaders will have to understand and prepare for the major hurdles along the way. 

Whether it’s ensuring that there is clean and reliable data to power a better decision-making process or utilizing data effectively with the right tools and people, Schmidt highlights the necessary foundations for data-driven organizations. 

 

What hurdles do Swarovski and other retailers face in their journey to become a data-driven organization?

One of the key elements for Swarovski was to clean up our internal data. When Swarovski started working with data, we also saw the need to improve the quality of data, as some of the fields were not maintained properly. And we have not been paying attention to data quality over the last couple of years as much as we should have, which affected how we used data.  

And to be honest, this was one of the key hurdles of data. That partially was just not updated and maintained. When it comes to third-party data from external sources, it was the same thing. There is a need to double-check if the data is clean and whether it makes sense to use it in a certain way. Here, it comes down to the people who work with the data itself, who process the data, and how they need to have an eye on data quality both internally and externally. There needs to be a process set up to ensure data quality and bring the organization behind this idea. 

That’s the first step. You need to have your data checked, and once you have that, you need to have the right team. The experts that can work with this huge amount of data, make it readable, make it understandable for the people in the business and create an outcome that is understandable and allows you to act on it. 

 

In terms of data adoption, what initiatives or strategies should retailers adopt? What can others learn from Swarovski’s approach to data utilization?

One of the key elements we work with quite a lot is GIS, Geo Intelligence Software tool. This strong partnership helps to understand the technical capabilities of the software, brainstorm with externals (without being influenced by internal structures) It basically helps us to make the most out of the system, learn and adopt fast.  

This tool makes data more tangible when it comes to geographics, store locations, to let us see the correlation between offline and online sales, develop distribution masterplans from it, and see consumer data and identify the market potential.  

As well as bringing “live” data to see consumer behaviours on a local level. This approach is very changeable because you can, for example, specify your data such as for the US on a county-level or a double digital postal code in Europe. 

 

Attracting Talents and Transitioning into a Data-Driven Organization 

As the demand for talents in data science continues to rise, retail leaders will have to find ways to attract better talents to handle the immense volume and complexity of data available effectively as they transition into becoming a data-driven organization. 

“Another enabler is having the right people in your organization. I always say, if you want to play Champions League, you need to have the best players.  A company needs to attract the best talents to be able to make best use of the data available and set the right action.” 

For Schmidt, the strategies for attracting talents do not have to be complicated and the main priority for retail leaders in becoming data-centric should always come back to the main foundations of clear governance, data, resources, and the deployment of talent. 

“Once you have the right people brought together, you can see the difference in results. There’s so much movement, but also the quality of the outcome is so good. Having the right people in place is a key pillar of the strategy because someone needs to process the data and work with the data.” 

 

How should organizations approach nurturing and attracting talent in data science? What initiatives or strategies should retailers push for?

I think what organizations can do is to provide a good workplace, culture, leadership, and have good employer branding. Having an attractive location, or an attractive working environment that also provides a good package for the people that lets you tap into those highly talented people and attract them into our industry. Make them understand the bigger picture, their contribution to the overall strategy, and let them do their job. 

The retail industry isn’t known for that background in working with huge data whereas other industries are more advanced in terms of data talent. But I think retail is such an exciting industry and there are really cool brands out there that want to work with data.  

If you provide the right work environment, good scope of responsibilities, and make work fun with the other teams. Not just purely data crunching stuff, because we sometimes forget how isolated working with data can be. Make it fun to work cross-departmental and celebrate successes together. 

 

What should be the priority for retailers transitioning into a data-driven organization? What pitfalls or mistakes should they avoid?

Again, I would repeat and emphasize getting the data right. That is one of the first things your organization needs to focus on. Have an internal assessment of where you are and fix it if there is something to fix. And trust me, we found topics to fix. 

The second is to define the processes and structures that works on a central level through all the necessary departments with clear roles and responsibilities. Then kick off with a strong external partner and talents to define the main topics you want to solve. If done in the wrong order, you process data, but you maybe won’t be able to draw conclusions.  

Another pitfall that organizations need to be aware of is to provide the right resources. Compiling data is the first step, step two is to make the data understandable, actionable and visualize it. And to do that, you need to allow resources for your people to work with the data.  It’s a time investment where you need to have people quantify, qualify, and draw conclusions out of the data. If you don’t provide the resources to understand the data, you’ll just wade through endless data with no action. 

I’m involved in a project where we work with heavy data and when the team is provided a fantastic tool to work with, we also need to make sure that people in the field have time to work with it and get the actions from it. Otherwise, you have this fantastic tool but you’re not able to get the benefits out there if your team does not have the time to work with it. So, I would say it’s about providing the time, the right resources in the field, to work with the data and earn your return on investment. 

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How to Equate Your Digital Transformation Journey to Sustainable Success

Have the following questions ever crossed your mind? 

  • What is the pace of digital transformation in my organization?  
  • Do recent digital initiatives align with the company’s overall business strategy? 
  • How far ahead are my competitors? 
  • What do I need to do to remain ahead of the game?

Matthew Bertram, a Future Point of View (FPOV) senior consultant, has answers to those questions. We were happy to host Bertram’s insightful presentation on digital transformation in a recent installment of 90Minutes CxO Insights — Fast & Furious Digital Transformation: Charting the Course for Sustainable Success vs. A Temporary Fix. Here are the highlights of his presentation. 

 

Digital Transformation: 50 Years of Digital Assimilation 

According to Bertram, the birth of digital transformation can be traced back to 2000. Although we are halfway through the age of machine intelligence, we have only just begun to scratch the surface. “We have AI and ML beginning to truly integrate with our society and our daily lives.” Bertram predicts that by 2050, society will be truly connected through the concept of transhumanism, “where you have computers and machines that are fully assimilated.”  

“One of the challenges that we have with digital transformation is risk.” Bertram explains the biggest risk in the digital business world is data extortion and AI attacks. “What you’re going to see is more of machine intelligence, extortion, and machine learning that targets AI. Whereas right now, attackers are targeting the data.” 

In addition, Bertram highlights that the rate of change in technology is not in tandem with the rate of change in people and organizations. Technology grows exponentially, while organizations grow logarithmically. He calls this the transformation dilemma.  

“What does that do as a leader? That creates a risk, it creates a gap between what’s possible, where we are, and where our organization is.” Bertram names this gap the Leadership Danger Zone. “This is the zone where we as leaders have to bring our organization up, we have to get them from where we are now, see what the capabilities are, and what it is in the future that we can move our organization to.” 

 

The Power of Pre-Emptive Vision

“Think about the technology that we’re talking about over the next five years or 20 years. You can break that technology down into five different categories.” The categories are conceptual technology, leading-edge technology, early technology, established technology, and mature technology. 

With each technology, there is timing, advantages and disadvantages, market reward, and market cost. When should leaders implement a new technology, taking these factors into account?  

“The longer you wait to implement a technology, the less risk there is in implementing. However, the reward from the market from being able to implement that technology goes down first, there’s a first-mover advantage. The goal is to be able to take advantage of new technology without having to be leading edge without being bleeding edge.” 

Bertram says leaders should have a good idea of conceptual technology five to seven years out, and leading-edge technology like blockchain, two to three years out. There are a lot of doubts about the metaverse, augmented reality, and specific types of AI. How do leaders know whether these technologies fit within their organization?  

Awareness, exploration, and adoption of new technologies

Firstly, leaders have to be aware of available technology, explore ways to bring that technology into their organization, and finally, make a bet to adopt the technology through investment. However, Bertram says that most organizations’ field of vision of the future is limited. They start exploring technology only to find out that they are already too late. “For example, Meta virtual reality. There are companies right now that are just now starting to talk about Meta, that are just now starting to wonder about this VR.” 

To avoid lagging behind the latest technology, Bertram calls for leaders to extend their vision as it is a critical skill set. “The way you do that is something that we call rivers of information®, which is simply a systematic way of learning.” 

“Winning organizations don’t look at what’s right in front of them today. They look at what’s coming out several years down the road and begin to explore. So right about the time it becomes leading-edge technology, they are prepared and ready to adopt.”  

According to Bertram, a combination of the rivers of information® and the high beam process has the potential to give leaders a two-year lead over their competitors.  

 

High Beam Strategy Process

Shared future vision 

“It’s important to be able to come to this shared understanding.” Leaders and employees should have a common picture of what the future holds for them and what it means for their organization. 

Trend identification and extrapolation 

Bertram says there are three types of trends to look at — macro trends, global trends, and industry trends. The goal is to identify the top three trends that are going to affect your organization. Once the trends have been identified, the next thing to do is to figure out whether these trends are hurting (anchor) or helping (sail) your organization.  

Develop a portfolio

This portfolio should include three types of investments — “Investments in people, investments in process, and investments in specific tools. Once you have this portfolio, once you have these investments, then you’ve got a way to be able to communicate this out.”  

Bertram stresses that this process should be repeated every year to assess the progress and development of technology investments and make any changes if needed. When executing a strategy or process, Bertram asks leaders to consider eight environmental factors which are culture, motivation, skill and will, key opinion leaders, personality styles, automation acceptance, tolerance for change, and market trends. 

 

Three Capabilities to Help Your Organization in a VUCA World  

The world is getting more unstable from a VUCA (Volatile, Uncertain, Complex, Ambiguous) perspective. “We’ve seen in the last few years what happens when you have a black swan event that totally changes all your assumptions that you did not see coming. How do you deal with a major global event that surprises you?” 

Bertram names three capabilities that are integral in helping an organization navigate a VUCA world. “No matter where you are in your transformation journey, these are capabilities that you need for your organization.” 

Citizen data scientist skill set

According to Bertram, citizen data scientists have a specific skill set to “fill in the white space that a data science team would not be able to catch.” 

The skill set of a citizen data scientist:  

  • Understand the purpose of data activation and the framework for excellence 
  • Thoroughly know your company’s data environment 
  • Understand analytics and how to enrich data insights 
  • How to use visualization, storytelling, and mashups to communicate with impact 
  • Using rules, triggers, and actions to automate insight 

From Bertram’s personal experience, organizations that are made up of 10% to 15% of data scientists have an enormous advantage over their competitors.  

Citizen application specialist 

The next capability is a citizen application specialist who has the following skill set: 

  • Understand concepts of process automation and the future of work 
  • Thoroughly know your company’s automation environment and tools 
  • Systems thinking, data and automation basics 
  • Sustaining automation over time 
  • Proactively solving common challenges of automation  

“An essential part of being a citizen application specialist is understanding how to proactively solve the challenge of displacement and what to do with reskilling and retasking humans.” 

Rivers of information®

The third and final capability is rivers of information® which aims to foster a culture of learning that is intentional and continuous. This is an initiative that needs to be constantly maintained and reviewed. Bertram says it is a big part of an organization’s upskilling program.  

“With rivers of information, it’s not just you as the leader. It’s not just your executive team. It’s not just your board. It’s the entire organization. This is typically driven by HR, sometimes by Learning & Development. As you implement rivers of information®, each department would have their own sources, reviews, and the ability to make this part of job descriptions.” 

 

The Next Steps

Bertram recommends that leaders start working on the following: 

  • Use high beam planning to get a clear vision of the next 3 to 5 years 
  • Identify investments in people, processes, and products 
  • Integrate citizen data science, citizen automation specialist, and rivers of information training into your organization  

“Building centaur capabilities into your organization is a huge advantage that augments any digital transformation. A centaur is a highly advanced human. Someone who has good EQ and good learning habits.”  

A digital centaur is an individual who has the skills of a citizen data scientist, citizen application specialist, and rivers of information® expert. When you pair a digital centaur with advanced technology, they know how to activate data, automate system processes, and continually learn to keep up with the latest developments in technology.