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Digital Transformation Success is a Myth

I have been working for a while now as part of digital and data transformation teams in large traditional companies, both as a consultant and as a responsible manager. There are big plus sides of such a job. You are working on topics that are considered to be “hot” and there are usually some innovation budgets you can tap into in order to finance your new projects. Its easier to find new jobs and negotiate good salaries as headhunting firms are looking for digital skills and people with experience on digital topics.

The job has also a big downside that most mainstream media outlets involved in generating the hype seem to underestimate. It is a dilemma that nobody I met so far can truly escape.

In simple terms put, you either appear successful at digital transformation by focusing on digital showcases or you try to drive real change (which takes an awful amount of time to show results in the best case or does not show much effects even after years due to the difficulty to change company culture). In a way its a lose – lose situation, which leads me increasingly to believe that Digital Transformation Success is a pure myth, at least when you look at it from the perspective of the person tasked to do the digital transformation. The simple truth is that you cannot really succeed in digital transformation. Let me elaborate on that in a bit more detail and also tell you how I adjusted my tactics based on that simple realization.

Case 1: Happy Honeymoon

In the first case (focusing on digital showcases rather than transformation), you most likely get some recognition in the beginning but it will quickly evaporate as you are not able to deliver to the expectation after a while. This approach brings short term benefits that look good to your upper management. I saw many digital labs and departments tasked with digital transformation producing one digital prototype after the other while neglecting the need to make the rest of the organization ready to be able to absorb these innovations.

Hence, most of the innovative ideas never made it into production or roll out, where the real business benefits happen. And even if they get there, they are simply rejected by the business departments as their are not ready to use them. Inevitably, after a shorter or longer while, the honeymoon period ends. Due to pressure from your executives to work on productive roll outs and since that requires you to fix some basic underlying problems, you are naturally shifting your goals towards driving digital transformation at the business departments. This makes you focus on the underlying pain points that prohibit the success of your great digital innovations you produce. Which leads us to case number 2.

Case 2: The Endless Road

In the second case (trying the hard stuff: transforming the company at its core), its even worse! You and your team run 12 hours and more a day to transform the company. If you do a poor job, nothing happens. Other departments will start to point at you and will ask you to justify your existence. If you do a good job at it, most people hate you since you are changing the company that they know and love. There is most often a reason why people work for a company. It is because they feel attracted to the products and culture. And you are here to change probably both at the same time (Sidemark: this in itself is the very reason why it is so difficult to execute digital transformation as you need to push the company on these two axis simultaneously). As a result, competing departments will try to convince your board that you are a waste of money, time and resources to slow you down in making progress as they fear to lose power and influence in their kingdoms.

To make things even worse: Why the disruptive nature of change does not help at all

Well, we are not done yet. There is an additional difficulty that you need to deal with as somebody working in digital transformation. Digital disruption has an exponential curve, which mans that it comes slowly without being noticed, but then turns suddenly and brutally your entire industry upside down (as in the case of Nokia and Kodak). Considering that your company is a traditional company, the digital maturity of your company is probably extremely low and it takes years to see the first results. At the same time, the revenues and profits of your company are still very high since your products are in the “Cash Cow” phase of their product life cycle. So, your executives do not really feel the pain of the digital disruption that is entering your industry. Perhaps they believe you that it is coming, but that is very different to real pain. And even then, it takes years of digital catch up until first results are seen. There are not many executives that are willing to wait that long as most of them will move into new jobs by then or get retired. Why should they risk their big bonuses today for something that does not impact them immediately?

As a summary, when you choose to go the endless road, even if you are the smartest and most effective person in the world, digital transformation will probably take too long until the fruits can be reaped and there is currently not enough “real pain” to make what you do attractive to your current top leadership. For them, it is enough that you do “digital showcases” to prove to the investors that your company is making progress and is innovating. Then, we are back at case 1. Which means that at some point people will start saying that your showcases do not bring real benefits and will stop supporting you. Well, does that not sound like the perfect vicious cycle?

Here are a few tactics that I learned
It is worth to think about tactics to solve at least partially the dilemma, especially when you believe that digital transformation is the right way to go for your organization. As part of my work, I started to apply three simple tactics that might help you as well. They won´t make the dilemma go away, but they can help to soften it.

1. The Mixing Cases Strategy: Combine some elements of each “Happy Honeymoon” and “The Endless Road” in every project you do, right from the very beginning. This will keep people happy while buying you time to do the long-term stuff. It might sound simple, but it is effective. The trick is to find the right mix. E.g., when you run an analytics pilot, also work on the data quality or data collection in parallel.

2. The Expectation Story Strategy: When you are doing digital transformation, never just simply say you are doing this and that because of this and that. Always tell a story where we are today, what the steps in between are and what the end game looks like. Make sure that from the very beginning you raise the right expectations of what your top leadership can expect at which point of time. This way they see even small steps as the right step towards a bigger goal, which your top executives can then communicate to shareholders and stakeholders (whatever makes them look good will make your live easier).

3. The Evidence Collection Strategy: Once you applied the Expectation Story Strategy, you should start taking base measurements and then start to take further measurements during every step to provide regular evidence to your leadership that your company is on the right track based on the expectations you have raised as part of your storytelling. Having evidence to show that corresponds to the expectation level of your storytelling in the beginning is a prove point that your story is right (at least so far). It helps to increase the level of trust of your upper management in the digital transformation activities during the next phases as you move towards the defined target state. Even if business benefits are not high during the phase you are in right now, people will feel more asserted that the high business benefits that come later are in the process of being achieved.

Happily Forever After

In a way we can draw a comparison to our private lives. At which point would you consider a marriage to be successful? Is it after a glamorous wedding and a honeymoon with lots of beautiful pictures taken on the beach and posted on Facebook? Certainly not. Just because you mastered the honeymoon, it does not mean that your marriage will last happily ever after. So, is the point of success after 5 years of happy marriage? Or, perhaps, rather after 20, 30 or more years? We cannot properly define what success means in the case of marriage as long as the couple is still alive. Nevertheless, we can perceive a marriage to be successful when we meet a couple after they have been through many ups and downs in their many years of marriage and still appear to be reasonably happy. We never know for sure, some myth will always remain! Maybe, its the same with digital transformation.

Smart Machine Marketing and the Algorithmic Economy

The reason why Smart Machines are so much more powerful than conventional computer programs are the advanced AI algorithms and the data that they can absorb. Smart Machines can sense their own state and their environment, can communicate with other Smart Machines, they are self-learning and can solve very complex problems, and they can act, sometimes autonomously. There are many technologies behind the capabilities of Smart Machines. The most important enabler is the massive amount of computing power and storage that is available today for a relatively cheap price, which makes it finally possible to apply computational heavy artificial intelligence algorithms that would have not been possible some years ago.

Many characteristics distinguish traditional software applications from Smart Machines. Computers have always been pretty good in repetitive and clearly described tasks and in applying strict logic and complex mathematics. An abundance of tasks today are solved by computers much faster, cheaper and more reliable than by humans. Yet, in many ways, computers appear oftentimes annoyingly stupid. Have you tried to have a meaningful and interesting conversation with a computer? It can be a difficult and typically very frustrating endeavour. What computers are missing is the ability to understand the meaning of what we have to say. This is because language is very ambiguous. The very same sentence can mean something completely opposite if said in another situation or by a different person. “I love this computer” could mean either did I really like my computer a lot but it could also mean that I really hate my computer because it doesn’t do what I want it to do. It is very unlikely that love refers to romantic love in this context. The idea that computers can think like a human sounds stretched, it is, however, closer than you might think.

It is changing with the up-rise of Smart Machines. It makes machines being able to handle situations with ambiguity, sparse information and uncertainty, thus, be able to solve human kind of problems. Instead calculating the optimal solution using a predefined algorithm, smart machines evaluate different options and choose the best option out of the possibilities. Problems do not need to be provided in a specified machine readable format, they can be simply formulated in natural language or even normal speech. Looking at the context of the problem makes it possible to interpret the question correctly. When I ask a smart machine “what is the best restaurant?”, it should understand that I am probably looking for a good restaurant that is not too far away from my current location. Based on the outcomes of an action, smart machines can learn and improve their problem solving. Instead of being programmed, they can read PDF documentations to understand a business process and observe how humans perform a business process to build its own knowledge base and eventually be able to handle the business process on its own.

The key components of a Smart Machine are depicted in the figure and will be explained in detail in the following. An incentive and rule system needs to be set for a Smart Machine which provides a purpose for the Smart Machine to exist (e.g. as a self-driving car) and the rules it needs to obey (e.g. ethics, law, company procedures, business goals).

Smart Machine Marketing Artwork

In order for machines to see, feel, hear, smell, and taste like human beings, all aspects of the physical world need to be translated into “digestible” data for machines to process, reason, and act. The rise of low-cost sensor technologies and the Internet of Things with its connected devices enables the collection of data from the physical world without human interaction. All senses are needed to cover an entire customer journey from inspiration to usage. The augmented senses of machines allow a broader, deeper, and more personalized customer experience. Sensed information is fed, interpreted, filtered, interlinked and used to initiate further activities.

The most important ability of Smart Machines is to process the sensed information similar to the way us humans process information (i.e. empirical learning). Smart machines are able to think and solve problems by understanding and clarifying objectives (and sometimes coming up with their own objectives), by generating and evaluating hypotheses, and by providing answers and solutions like a human would do (and unlike a search machine which gives a list of results). Smart Machines are self-learning, they can adapt their own algorithms through observing, discovery and by doing.

Finally, Smart Machines can act, by visualizing and providing the responses to a human decision maker, by informing or even commanding a human to execute certain activities, or in the extreme case, by completely autonomously executing a business process or any other actions. Based on the results of the actions, Smart Machines are able to re-calibrate their goal setting.

The impact of Smart Machines will be observable in three domains for Marketing Professisonals. First of all, customers will get a more contextualized and personalized experience. Secondly, the marketing departments will be able to do more with less people building on automation and scale of intelligent algorithms that take over some of the human labor. Thirdly, there will be advances in the customer journey possible which are of disruptive nature.

The marketing profession will be impacted fast and significantly by Smart Machines and the Algorithmic Economy. Personalizing and contextualizing the customer experience is the aim of everyone. But, creating meaningful continuous 1:1 interactions can only be feasible on a large scale with thousands or millions of customers if Smart Machines take over a lot of the work. This means that smart machines take over work reserved for humans in the past, as, for example, generating new content, and supervising staff in retail stores to ensure high customer engagement. It also means that those companies that still struggle with data-driven marketing will be in deep trouble. Those who embrace Smart Machines will be able to drive productivity beyond the imaginable for marketing and sales within the next decades.

Like all things in life, Smart Machines are all a matter of perspective. For marketing divisions in traditional companies, they might be seen as the biggest threat in history. The way most marketing departments work today is very reliant on human labor and decision making. Shifting the work to Smart Machines will make a lot of the abilities needed for traditional marketing personnel redundant and will require new capabilities that the workforce does not necessary have. For others, Silicon Valley startups and companies, Smart Machines generate an once in a lifetime opportunity. Smart Machines enable them to scale their limited resources and, thus, be able to challenge even the largest established players in their own strongholds, irrespective if it is retail, consumer goods, banking, insurance, manufacturing, entertainment or any other type of industry which requires Smart Machine Marketing.

Is it Time for a 2-Speed Business?

Shortly before my summer break – a lovely holiday in Northern France – I gave a keynote at a data science event that highlighted the importance of a bimodal IT for digital innovation.

The key idea behind bimodal IT is that IT needs to offer a second mode in addition to traditional IT that is more risk taking, agile and customer-centric in order to drive digital & analytics innovation more effectively.

Mode 1 is characterized by Gartner as the traditional mode of IT, which has a focus on reliability, is plan and approval-driven, uses large enterprise IT suppliers and typically follows a waterfall approach for implementations.

Mode 2 emphasizes agility and, hence, uses agile implementation approaches, it utilizes often small, new innovative vendors and works closely with the business to drive fast and frequent customer-centric business innovations.

There are many organizations that have started to establish a second, more agile, mode of IT (e.g. in form of a data science lab, a digital factory, or an agile development and DevOps department) and they usually run into two major challenges which impede them to reap the expected benefits:

(1) The two modes of IT are not synchronized well enough

(2) Business is not able to engage effectively with agile IT

I will explain these issues in more depth in the following and some lessons learned how to resolve them.

(1) The two modes of IT are not synchronized well enough

What many organizations get wrong is that they focus to much on creating the new agile Mode 2 of IT.  However, this is only one component of implementing a bimodal IT. The real challenge is how to synchronize both modes so they can play as a team. Having them in silos will not only create conflicts, but also will limit the success of any projects that need both Mode 1 and Mode 2 resources to succeed – which is rather the usual. So, what organizations need to establish is a bridge between the two modes.

Practically speaking, it all starts with mutual understanding and respect between the two modes. If Mode 1 resources have the feeling the they are a second class of IT, they will stop supporting Mode 2 and hinder them wherever possible. Leadership needs to communicate that no mode is better than the other, and both modes of IT are equally needed for success. Mode 2 resources need to understand that Mode 1 is crucial to renovate the core of IT, which enables innovative digital apps to be built on top of a healthy infrastructure efficiently and securely.

Moreover, there are touchpoints between Mode 1 and Mode 2 that require bimodal synchronization through explicit governance:

~ When a new application is planned to be developed, selection criteria have to be defined that outline which implementation should be done in which mode of IT.

~ When a new Mode 2 implementation project is starting, it has to be examined if interfaces to Mode 1 applications are needed and/or if other Mode 1 resources are required.

~ In particular, when the Mode 2 product is supposed to be released in a Mode 1 production environment, traditional release management needs to be involved already in the beginning of an agile project.

~ Finally, when a Mode 2 product is released, there might be a decision to further manage it in Mode 1 in the future.

(2) Business is not able to engage effectively with agile IT

Today´s businesses are not ready yet to engage with Mode 2 IT in a productive manner. This has two main reasons.

First, the second mode of IT is all about experimentation. Trying out new features, new approaches to analyze data and new ways to interact with customers, and taking into account that many of the experiments will not turn into viable products after all. Today, most traditional organizations have not developed a mindset for experimentation yet.

Second, using agile IT methods requires a much more intense participation of business during IT projects. Business is used to “throw business requirements over the fence” and IT would take them, take a few months or even years to implement them, and would come back eventually for testing. In the meanwhile, business does not need to spend much time for the  IT project. This is not the case for agile projects. In each sprint, the business needs to closely work with the developers and defines the business requirements on the run during the project.

These two points highlight some of the obstacles that come up, when there is a two speed organization on the IT side, but only a one speed organization on the business side. The solution is simple, but substantial: Many large organizations that I work with have recognized the need to establish also a second mode of business, which is more experimental, fast paced and enables real digital innovation.

The consequences are visible: There are more and more business labs and business innovation centers of large enterprises popping up around the world in addition to data labs that have the role to work with agile IT to come up and test new innovative ideas in a fast mode. They aim to imitate a startup environment  where creativity, experimentation  and disruptive innovation is in the focus. The results are impressive so far. Mode 2 IT can be much better utilized and the collaboration between business and a bimodal IT becomes significantly better when a two speed business has been established.

This is only the beginning, but one new imperative clearly emerges: It is time for a two speed business for any organization. The pace of change will become faster and volatility will increase in the future. So, let´s get business ready for it.

 

Dr. Alexander Borek advises Forbes 500 companies in multiple industries with regards to their digital transformation, data governance and Big Data Analytics innovation strategy.

All opinions in this blog are written in private capacity and do not express or reflect the opinions of his employer.

Industrializing Data Science and Analytics

Gartner´s “Hype Cycle for Advanced Analytics and Data Science 2015″ has just been published. The trends indicated in the hype cycle show a rising maturity of this young organizational discipline.  It is interesting to see that the buzzword “Big Data” has finally disappeared from the hype cycle, while machine learning (a discipline that has been there for decades, at least in academia) has reached the peak of inflated expectations.  This underpins a tendency to  move from big data (the bigger the better) to smart data (the smarter the better). Simply said: “No matter if it is big or small data, it is still data and we aim to get more value out of it.”

A trend that is also visible at a second glance is the emerging industrialization of data science, which is underpinned by a number of developments. Vendors increasingly support the management of analytical models built by data scientist over their entire life cycle, when they are scaled from prototype to company-wide adoption. So far, the management of analytical models has been rather disorganized in most companies.  Data scientists would create new models on an use case by use case basis. Some of the models have been actually doing what they promised to do and would be deployed in operations.

An end to end management of the models and a reuse of solution patterns for analytical models across the enterprise has not been actively enforced or governed. In a new project, they would often start nearly from scratch although a similar model might have been already developed in a different business unit. From an organizational point of view, it makes sense to have a centralized data science unit that can support data scientists in decentralized business units. A central data science unit can ensure that learnings are incorporated and fed back to the organization and that analytical models are consistently governed even after they are handed over to IT.

Very connected to this is the concept of the model factory. The idea is to bring automation and scalability to the process of building and deploying predictive models.  To find the best models, a huge number of models are built and tested using software tools that provide a high  degree of automation during devleopment. At the end of the process, only the best few models are deployed.

Finally, a thrilling concept comes from Gartner´s Alexander Linden, which is the concept of the analytics market place. Some companies such as Microsoft, Rapidminer and FICO have created marketplaces, where data science services and additional functionality are provided by third parties which can be purchased by users of the analytics platforms. This can become a true game changer. Similar to the third party apps and services provided at Salesforce.com, analytics marketplaces could become a source of millions of very domain specific analytics micro-applications that drive innovation.

Today, we stand only at the beginning. I am convinced that in a few years time, data science and advanced analytics will be as industrialized as traditional IT. What has changed with the uprise of data science is the speed with which new applications are developed and deployed, the increased willingness to experiment and the direct way data innovates business models and business operations. Now, we only need to scale it to the rest of the enterprise to reap the full benefits.

 

 

Data Quality Expert Panel at DGIQ 2015 in San Diego

I really enjoyed the Data Governance and Information Quality conference that took place in mid June in San Diego. There were many great talks, a highlight was Anthony Algmin  who talked about his first 100 days as the new Chief Data Officer at the Chicago Transit Authority.  A great keynote was given by Scott Hallworth about the data quality journey at Capital One. Nancy Fessatidis of SAP gave a keynote on an emerging topic that gets a lot of attention these days: the ethics and morality of big data. The panel on controversial issues in data governance had been a great ending of the conference.

During the conference, I gave a half day tutorial on “setting up a data quality risk management program at your organization”, where I could enjoy a very active and interested audience. Going to a lot of data conferences, I can observe a rising level of interest over the years in applying the risk paradigm to data quality, especially from regulated industries like banking and insurance.

I also participated in a very interesting panel discussion on data quality best practices with Michael Scofield, Peter Aiken, David Loshin and John Talburt, in which I have highlighted the role of business outcome focused data quality metrics.  You can watch the video of the panel discussion below.

Frontend Versus Backend for Digital Innovation

Very simply speaking, business processes can be divided into two categories, namely, front office processes, which are all customer interfacing business processes and, back office processes, which are all business processes that have no customer touch points.   The back office is usually what the customer does not see.

Let me use a simplified exemplary scenario to explain how all these things interplay:

A customer finds a red wardrobe in a catalog and would like to know if his nearest furniture shop has this particular product available to make sure he does not drive 35 miles to the store for no reason. The front office business process in this scenario is that the customer asks the question if the product is in stock and gets the answer to his question. To answer his question, we need to know which products are available at any given time. Hence, there is also a back office process required, which is to keep track which products are in stock and which ones are out of stock at the moment.

If the front office process and the back office process are both not digitized at all, the customer has to give the store a phone call and hope that some staff member will pick up the phone, go to the shelf where the product is stored and checks visually if there is still a red wardrobe available for sales.

Examples Front office process Back office process
Not Digitized Process Customer gives the store a phone call. Staff member picks up the phone, checks if the product is available Staff member goes to the shelf where the product is stored and checks visually if there is a red wardrobe still available for sales
Digitized Process Customer types “red wardrobe” and his address at the web site of the furniture store and it shows that product is available in the nearest store All products contain an RfiD chip that can track them on the shelf. IT System can provide real-time availability information to staff and customers.
Automation of Business Process Website makes call obsolete and staff does not need to take an additional phone call RfiD tracking of stock instead of visual check if product is available
Digital Data Generation Customer address and product of interest is captured Stock level and availability for each product is captured
Digital Data Usage Data about availability of red wardrobe is used to answer request Data showing which RfiD tag is linked to which product is used to track stock level

In contrast, if we want to digitize the front office process, we could create a website through which the customer can check if the wardrobe is in stock. The website would automate the business process in the front office. By typing the product name of interest at the website, the customer provides this information in a digital format. The result comes back on the screen, which uses existing digital information about product availability in store. The data about product availability could still be entered into an IT system and maintained manually by an employee in the back office. The customer would not notice if the back office process is digitized or not as long as the information is up to date.

Finally, if we want to digitize the back office process in this scenario, we could, for instance, automate the tracking of products in the shelf by putting RfID tags on each product (RfID = Radiofrequency Identification). An RfID reader can then wirelessly detect how many products are on the shelf at any given time and store this information in an IT system. The IT system can provide this information to the website so it is visible to the customer. But the front office process does not necessarily have to be digitized. Even when the customer calls in, the staff member still saves time. The staff member would not need to make a visual inspection to capture the stock level as he can look up the product availability in the IT system.

Even if your customer does not see what is going on in the backyard, digital transformation of your back office is very important to your business success. A great customer experience is often not possible without efficient and effective back office processes. In our small furniture shop scenario, when the stock level and availability for each product is captured digitally, it is ensured that the customer has always accurate information in real time. Secondly, making your back office running more efficient with digital transformation can save you a lot of costs and make your operations run smoother and leaner. In our small example, you would need a lot of additional service staff that answers service requests. And thirdly, digital transformation makes your back office more effective, which can help you, for instance, to optimize your supply chain management, to prevent fraud, manage business performance better, optimize your physical assets, create the highest value with your human resources and better manage your finances.

In essence, executives should avoid to focus all their digital innovation efforts only on what is shiny and visible to the customers, the inner core of your business can be an even stronger competitive differentiator, even if that is not directly seen from the outside. And not everything that shines is gold.

Free Digital Lunch for Retail Banks

“When I go to Silicon Valley…they all want to eat our lunch. Every single one of them is going to try.” Jamie Dimon, Chairman and CEO of JPMorgan Chase

The pressure to innovate with digital technologies is enormous for banks. Yes, the competition is tough between existing banks. The biggest threat comes yet from outside the banking sector by startups and companies that never knew the pre-digital era.Tunde Olanrewaju, principal at McKinsey´s London Office highlights where the focus of digital transformation for banks lies: “Most of the potential value in digital banking comes from the impact on the cost base, particularly in the areas of automation of servicing and fulfillment processes and migration of front-end activity to digital channels.“ When you want to provide shinny new digital customer services to your customers as a bank, you have to sort out a lot of the back office challenges first. 60% of customer dissatisfaction sources and 10-20% of contact center volumes originate from execution issues in the back office.

Some of the results banks are achieving through digital transformation of the back office are truly game changing, for example:

  • Managing an end­ to ­end payments solution
  • Automating manual controls and processes
  • Simplifying the business by moving away from non­core products
  • Consolidating operating call centers
  • Optimizing the branch network
  • Reducing cost of service without reducing quality of service
  • Product back-office automation
  • Document-management digitization
  • Automation of credit decisions
  • 24/7 availability via interactive voice response

One way to achieve operational cost reductions is to get rid of the old branch structure. For example, piloted in Malaysia, the Philippines and Singapore, Citi Bank launched a new global initiative called Citibank Express, which is a next-generation ATM that allows clients to access nearly all the services available at a traditional branch. 

Barclays Bank, as many large banks that combine retail and investment banking, had to face a lot of mistrust by consumers in the aftermaths of the global financial crisis. And Barclays knows that cultural change  at the bank is necessary. “We were too aggressive, we were too short-term focused and too self-serving,” admitted Barclays CEO Antony Jenkins in an interview with CNBC, “The industry, and Barclays, got it wrong on occasions.” And digital technology is an important element for Barclays to restore trust with its customers.

In 2013, Barclays created two top positions for its technology driven transformation, a group chief data officer and group chief digital officer. Usama Fayyadd was appointed as chief data officer. He is regarded by many as the first executive ever to be titled chief data officer when he took on the role at Yahoo no less, back in 2004. Although a high profile executing, Fayyadd had not much previous banking experience. The firm-wide governance of data is something that is being looked with a view to simplification and gaining both efficiencies as well as a better overall quality of data. Shadman Zafar, who holds the position of the chief digital officer, has previously worked for mobile network giant Verizon.

“What is happening is that customers are becoming much more used to using technology and want to use technology to deal with their banking. Why not check your balance on your smart phone? Why not pay a check on your smart phone? That means that over time we are seeing a shift how customers do business and technology allows us to serve them where and when they want to be served. […] I think we should accept that, over time, there will be more and more delivery of our services by technology”, says Jenkins. “Barclays has spent time and money on mobile – and it shows”, comments Forrester analyst Stephen Walker. In 2014, Barclays has been recognized to be the top mobile banking provider. Moreover, 6,500 cashiers from Barclays branches are trained as a new breed of ‘community bankers’, who, armed with iPads, will help customers use automated machines. A new voice recognition system and a voice-biometrics security system is planned to be rolled out to its 12 million retail customers in 2016.

Banks have to live with a lot of legacy systems. In fact, up to 90% of the average bank´s IT budget is spent on keeping up those systems. To avoid replacing the legacy systems, banks have often built new applications on top of old ones and added new interfaces. A typical retail bank has to manage and monitor between 300 and 800 back-office processes. Many of these tasks are redundant tasks, create excessive manual processing with slow response times. The potential for digital transformation is huge. Automating and digitizing processes can help to mitigate the risks of human error and reduce paper consumption costs. It can lead to leaner channel and organization structures, a streamlined governance, a more agile culture and an enhanced revenue model.

The Pressure for Traditional Companies

Characteristics of a new digital world

The world is changing rapidly and becoming digital. Digital technologies fundamentally change how we live, work and interact and will also transform the basis of competition in most industry. This can make “the physical world better, worse, or just different”, as Eric Schmidt and Jared Cohen describe it. What are the characteristics of this new digital world? We can observe three major trends which will be laid out in more detail during the next paragraphs. The physical world is becoming rapidly more instrumented and interwoven with the physical world. Having all the information about the world digitized allows computers to analyze this data with speed, precision and context-awareness, providing a new source of intelligence and automation. MIT researchers Eric Brynjolfsson and Andrew McAfee have announced the second machine age: “Now comes the second machine age. Computers and other digital advances are doing for mental power – the ability to use our brains to understand and shape our environments – what the steam engine and its descendants did for muscle power”. As a large proportion of the whole planet will be equipped and interconnected with smartphones and integrated mobile computing devices at home and everywhere else in the not too far off future, this new intelligence will be fully integrated into our lives.

When I talked to top executives of incumbent leaders in traditional industries such as banking, insurance, consumer products and manufacturing, they all admitted that their biggest threat for their companies future they see are digital savvy companies like Google, Facebook, Amazon and Apple and new technology startups from Silicon Valley and other innovation hubs.  CEOs have carefully observed how new digital players have exiled established players in the retail, music and TV industry and they fear that the same will happen to them. The new wave of digitization does not stop at the online channel. It includes every part of our lives through the new mobile channel, social media and the Internet of Things (sensors and chips hidden in traditional products).

Companies have entered the digital race

Data and digital technologies are becoming the new major source for productivity, competition and innovation. Collecting, combining, analyzing and using the large volumes of data available to us can provide companies with such valuable insights that it can be a true game changer in nearly any industry. But real change comes only, when the new data and insights are fully integrated into the business processes of the company, and when customer experiences and the underlying business models are redesigned. So, if you take away only one thing about the digital world that is evolving, this should be it:

The most important imperative for business leaders is that data will be the basis of competitive advantage across all industries and that companies need to digitally transform to reap the benefits

Naturally, data driven innovation and digital transformation get a lot of C-level executive attention today. Many companies are embarking on a journey to transform their core business processes with data and digitization. It is not enough to simply set up a single project that looks at disruptive technologies to compete in this brave new world. Leading companies have started to re-think their entire business.

Personalize Marketing with Digital Analytics

Once upon a time, everything was personal. When you went into your local bakery, you would hear something like “Nice to see you again, Mr. Richards. Since it is your son´s birthday in a few days time, would you like me to bake a birthday cake for him?” Today, every customer is one in a million of other customers. So, here is the trick question: how can you personalize marketing to thousands of your customers? The key is data. Digitize every touchpoint with your customer to collect data. Listen to the voice of your customer on social media. Give your customers rewards and incentives for sharing data with you. Look for data that explains the local context of your customer interactions, such as local news, events, economic situation, prices, culture, weather and political opinions. And integrate all that data to learn insights across all data silos.

Every single interaction with your customer matters. Use the data and insights to personalize every touch point that you share with your customer to improve the brand experience. Give your employees the power of analytics at their fingertips so they know what the customer has done online and on the mobile phone when the customer comes into the branch and telling the employee what the customer likes and what better to avoid. And use cognitive technologies that can fully personalize the automated interaction on mobile phones and online.

Then, you have to learn how to predict things that are relevant to personalize the customer experience. First of all, you have to creatively construct features as the raw data is not ready for analytics. For instance, calculate the age of your customer using the date of birth. Determine the product categories bought, the frequency of purchases and total revenue from transactional data. And turn the profession into an average salary for this type of profession, for example. Transactional data combined with customer and contextual data is extremely powerful. Predict the psychological profile of your customers. Predict where your customer are going and what they need at this very moment. And predict the next best marketing action for each individual customer.

Type of personalization Example
Personalize style for your communication “I am a fact driven person. So, please, just give me the facts.”
Personalize channel for your communication “We prefer personal interaction when it comes to our mortgage.”
Personalize content for your communication “You should know that I am a vegetarian.”
Personalize context for your communication (e.g. time, place) “I am on my way home and looking for a great place to eat.”
Personalize your service “I like action movies with Sylvester Stallone. No nonsense.”
Personalize your Product “My car can speak to me and knows my favorite restaurants.”

 

With the right digital tools, you can personalize nearly everything. The style of your marketing messages. For instance, your customer might be a fact driven person and expects you just to give him the facts. The preferred marketing channel. You might find out in your data that young couples that plan to buy a house might prefer personal interaction when it comes to choosing the right provider for their mortgage. The content of the marketing message. You better avoid selling meat to a vegetarian. And the context of the marketing message. An add for a restaurant works best, when I am hungry on my way home from work and looking for a great place to eat.

Besides your marketing, services and products can be personalized, too. In the old bakery times, a personalized service could be something like you coming into the bakery and the baker you´ve known for a long time says: “I noticed that every Wednesday you buy four pretzels. I have reserved some for you as we were nearly running out of stock.” Today, a personalized service is given to millions using customer data analytics, for example, by media streaming service provider Netflix that knows that you like action movies with Sylvester Stallone and maybe some movies with Bruce Willis, but certainly no romantic nonsense like Titanic or Shakespeare in Love. Personalizing your product in old time in the bakery could be your baker saying: “Last time you told me that you like bread with sesame sprinkles. I made some especially for you!” Today, it might be your BMW that calls you by name and knows your favorite restaurants

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Big Data – Big Risk: Why Companies Need Total Information Risk Management

In our book “Total Information Risk Management: Maximizing the Value of Data and Information Assets”, we argue that data has become the major source of risk in most industries. Never before in human history, data could create so many opportunities and do so much harm to an organization`s success as today. Data has penetrated quickly into every corner of our society. We use data in higher volumes, of higher variety and velocity and from many new sources like social media and embedded sensors in real-time to drive a majority of our decisions. It has become the most important asset of the 21st Century, sometimes referred to as the “new oil”. Yet, the rising importance of data and information assets makes it also the major source of risk for most companies. Oil can catch fire. When companies finally start to understand the dangers sleeping in poor data and information assets often accumulated over decades and combined with new data from a variety of untrustful sources, it is often already too late: massive mis-investments, huge regulatory fines and permanent brand damages are only some of the consequences that cannot be easily undone once they happen.

Total Information Risk Management is a step by step guide for managers to identify and quantify the business impact of using poor data on business process performance and organizational success and how such risks can be mitigated. Solid measurement and quantification of data and information risk enables companies to generate real accountability and to treat data and information assets seriously and more responsibly. It also gives a great basis to build a convincing business case for data quality improvement.

A very typical situation is, for example, a manager who asks: “How many new sellers do I need to hire to meet my targets?” And the business analysts would come back after a while with the precise answer: “our analysis reveals that 3520 new sellers are needed”, which would lead the decision makers to reply: “Ok, this is interesting, well done, 3520 sounds very reasonable. But how reliable is the data?” The business analysts would assure that the analysis is rigorously conducted using data that comes from a system which is considered as a trusted source by most of the departments. The leadership team, being fully satisfied, would announce the new targets to the rest of the organization: “We need to recruit 3520 new sellers in the next quarter. This is grounded on a rigorous analysis of our business analysts!”

But, what if these numbers are wrong? Who would have time to verify if the methodology and the data that is used to calculate the results are indeed trustworthy and of high quality? And who would dare to question such “hard” facts indeed? And if something goes wrong, management can always refer to the business analysts. And business analysts can easily blame the data behind the analysis, the general complexity of the problem, and other external factors that influence the outcome of the decision.

Literally, millions of the most important decisions made by companies are executed exactly this way – every day. And an incredible amount of these decisions are mislead by poor data and sub-optimal analysis, leading to immense costs and risks in these organizations. There is a general lack of accountability and this is why huge risks are created in companies day by day – and why nobody addresses the true root causes of these problems. The formula is simple: Bad data leads to bad analysis, which leads to bad decisions, which leads to risks in operations and strategy.

So, how can risks from poor data be prevented? Companies can only protect themselves and make data and information reliable assets, if they start measuring the risks created by not having the right data and information of sufficiently high quality. Assessing risk caused through poor data and information assets will make the potential data and information risks tangible and visible to anyone – impossible to be ignored by the business part of the organization. Risk mitigation can then address the causes of the data and information risks with a targeted mix of technologies, transformation of the business environment and suitable information governance.

Leading companies are not the ones that simply use data to drive decision making, but those companies that assure that the risks hidden behind the data are clearly understood, measured and managed pro-actively.