I gave a keynote at the Data Insight Leader Conference in Barcelona this week speaking about the 10 rules of data transformation in inherently traditional industries based on the experiences I have made so far. Interestingly, there was an agreement among the Data Executives in the room that we need to keep doing showcases while transforming the company and it all needs to be wrapped up in a powerful narrative.
Here are the ten rules that I presented.
Rule 1: Accept that digital transformation success is a myth
As discussed in my last blog post, there is an inherent dilemma with digital transformation. In simple terms put, you either appear successful at digital transformation by focusing on digital showcases (happy honeymoon) or you try to do the real transformation changing how the company operates (the endless road). 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.
Rule 2: Demonstrate how basic beliefs in your industry are turned upside down due to digital disruption
A major mistake that many Digital Transformation Executives do at the beginning is to assume that others understand and share the basic beliefs about the success factors and changes in their industry. But they don´t. Others in your company, which includes the top management, have been running their company based on the same unchanged beliefs for decades. Such beliefs consist of things that have been true for a while. Examples are the standardization of processes to reduce cost, hardware product centricity to ensure product quality and attractiveness, number of sold products as key KPI and the number of physical stores is a reflection of market power. Digital disruption suddenly turns the world upside down. It might be that software engineering becomes equally important to hardware engineering and manufacturing. Data & Analytics become a central part of product quality and customer experience. Its not about physical stores but digital touch points. At the very beginning, the CDO needs to set the scene and also explain its implications and then ask directly for the changes to be implemented to match the implications. It is important to tell your senior management explicitly how basic beliefs are turned upside down due to the forces of digital disruption and which implications this have on corporate strategy, organizational structures, KPIs and incentive plans that need to be introduced or adapted. Complementary implementation projects which demonstrate value and roadblocks can be of help that can be discussed as tangible example.
Rule 3: Communicate the simple equation: Digital = Data + X
This is the most important formula for Data Executives. Communicate it at all times. Anything digital is a result of data (collecting, using, combining, analyzing data) and something else on top. It means that there is no digital transformation without a data transformation. Any Chief Digital Officer that says we will deal with Data & Analytics later since we have other more important priorities for digital transformation at the moment misses out that anything else digital he wants to do requires Data & Analytics. Unfortunately, in very product driven companies this happens very often. Communicating the magic formula constantly and explaining it with tangible examples reminds everyone around us that data is a key ingredient to any form of digitalization effort and digital product. The fact that we always need something more than data & analytics puts any data executive in a strategic disadvantage. Simply put: If others don’t do their job, you are screwed. So you better choose projects were you can rely on the X! The best algorithm to determine the optimal pricing of goods sold does not add much benefit, if the results of the algorithm are not used inside an e-commerce portal to improve pricing. This requires changes in the e-commerce portal itself and the processes around it.
Rule 4: Train your existing workforce in data analytics – everyone can learn it
It is naive to think that you can hire all data scientist from other companies. You can hire a few experts, the rest you need to train. And its not only the data scientist you need to train. You need to train also the data and digital project managers and program managers and the people that steer them and their top management. They all need a better understanding of what it needs to build a great data product. A lot of culture change comes through this type of education. It is therefore absolutely essential and not a side activity.
Rule 5: Ask the board to delegate decision making power to cross functional data analytics roles and bodies
The board cannot decide on all data aspects. They need to decide on the governance framework and strategic decisions, the rest of it needs to be delegated. Here the problems start. These decisions are delegated to individual departments such as sales and production. Customer data decisions are delegated to the customer departments, production data decisions are delegated to production departments. This is wrong. Decisions on data should be ultimately taken by cross-functional committees and cross-functional data roles and not individual departments. One data producing department might not see a need to provide data in a well readable format to a data consuming department. Creating the cross-functional committees and roles for data and analytics is one of the first things you should do as Data Executive.
Rule 6: Free your data
Everyone in the media talks about the large volumes of data that are available to be analyzed for all sort of purposes. The brutal reality in traditional companies that we often find no lakes at all, instead a vast data desert. Data is locked away in hundreds of different legacy systems that cannot be used due to their instability and risk of impacting running operational applications. This is something that top management does not perceive since they get all the analysis they ask for (within a day). Even worse, company politics prevent data from being shared. And GDPR and other compliance challenges make this even more difficult. Any project suffers under it. Any. Show to your board the need to create an electronic workflow to regulate data sharing and access involving all control functions like legal, security and data protection with concrete examples. Document how long it takes to get the data and what the problems are GDPR and other approvals. Create a virtual or physical data lakes with an integrated access layer to provide the data to the data scientists and data users. The workflow has prechecked criteria and categories. Instead of having individual decisions all the time by the control functions, they preapprove some data usage for some purposes.
Rule 7: Share knowledge on data
Our romanticized view of what data scientist do all day is that they create complex statistical models and algorithms, apply deep learning and other sophisticated machine learning methods. Wrong. Data scientists in traditional corporations search for the right data all day long. Once they find it, they need to find out what the data means by trying to set up meetings with business and IT departments that do not see helping the data scientist as part of their regular job activities. Its more like their hobby or welfare activities. Just let everyone finding out things document it in the business glossary. Its a give and take model. You document and next time you can find something that others have entered. People in the business departments have also an incentive to feed their data knowledge into the system, since they have less work with explaining data to each new data projects. At the end, all we need is some way of tracking these activities as part of each project and some good peer pressure. This usually reduces around 30-40% of the data scientists and data project workloads. The world can be so simple.
Rule 8: Automate data preperation
Another 20-40% of workload reduction for the data scientist and data projects can be realized if we automate some of the most tiring tasks of data scientists: data preperation. Let´s say you run 50 data projects a year in your company. Lets assume that 20% of the data processing work could be automated once you solved it for the first time. If you only half of the time and resources to automate these data preparation tasks, you have a pretty good business case. After a while, you will realize the full 20% savings.
Rule 9: Embrace an open source, cloud and AI first strategy
In the data and analytics space, a lot of the great tools are open source. The good thing about open source is that you can use it across the entire organization and often more Cloud ready than many commercial software tools. Students from university typically know the tools and there is loads of training. They are much better interoperable with other tools. And you can replace them much faster once they are out of date. This all speaks for an Open Source first strategy. Especially in data science, data scientists need fast access to tools and data with flexible computing power. Hence, adapting a Cloud first approach is the best way forward for most companies. The best way is to use a hybrid cloud approach (private and public). AI assistants like Siri, Alexa and Cortana are currently reshaping the way how we interact with machines using natural language and automating business processes and decisions in the background. Building new applications should therefore follow the AI first paradigm, no matter if it is about internal process optimization (e.g. IT helpdesk) or customer facing applications (e.g. customer support).
Rule 10: Balance data analytics innovation and transformation
Last but not least, combine some elements of each “Data Innovation” and “Data Transformation” 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.
The fascinating aspect to me is that doing digital transformation is one of the most difficult tasks that anyone can get. Nobody likes change. You receive so much resistance and people try to politically kill you. Still, most people that I know working in this field are passionate and love their job. They have the feeling that they do something with a higher meaning. Their jobs can have significant visible impact in the most positive sense after a few years. You don´t necessary get the recognition for it. But you can see the result after a while and be proud.