For the last fourteen years, I have worked with Data in one way or another. I started out as a Management Information Systems Manager (ABN Amro), a great title but I basically downloaded PDF reports and manually entered them into a spreadsheet to produce a Daily Financial Report, and through the years I have worked as a Business Intelligence Manager (ING Bank, Rabobank, Delta Lloyd), Data Analyst (Microsoft), Data Scientist (Adyen, De Bijenkorf) and now as the Head of Data at a Dutch-based Payments Technology Startup (Dimebox).
For all my experience, the thing that stuck with me the most are the times that my work actually made an impact. Of course, when you work in a company that has over 15,000 employees, your impact is very small compared to when you are in a 30-person startup, but knowing that the work that you did helps to make a decision, is probably one of the best parts of working with data.
But as data and especially Data Science and Analytics has become the latest hot topic, it surprises me how many companies, rush in trying to attract Data Scientist, Data Engineers, Machine Learning Engineers, Artificial Intelligence Engineers, but never stop to think about their Data Strategy.
In the past data has been perceived as a byproduct of a business activity or process, for example customer data collected during the sales process is stored in a CRM system and rarely used unless there is a need for follow-up (e.g. customer service, special reports or audits, etc.).
Fast forward to today, businesses recognize that data has value, especially transactional data which can be used for reporting or data analytics, which can lead to better decision making. But even though the perceived value of data has increased over the last two decades, many companies still struggle with capturing, sharing and managing data, because they behavior reflects an outdated underlying belief that data is simply an application byproduct and therefore hire a “Data Person” to do something with it. Because hiring someone for the sake of hiring, never fixed anything, I believe that organizations need to create a data strategy that matches today’s reality, before thinking about hiring anybody with Data in their title.
The idea behind developing a data strategy is to make sure all data resources are positioned in such a way that they can be used, shared and moved easily and efficiently. In other words, having a data strategy ensures that data is managed and used as asset and not simply as a byproduct of the application. By establishing common methods, practices and processes to manage, manipulate and share data across the company in a repeatable manner, a data strategy ensures that the goals and objectives to use data effectively and efficiently are aligned.
Unfortunately, just as most companies are still using data as byproduct, the way they try to resolve their data problems, is not by devising a Data Strategy but by hiring a Data Analyst. So instead of having a clear objective to turn data into insights, most Data Analyst get access to the database and get requests to run queries that any basic analytics tool could provide in seconds.
Lately, the trend has shifted from hiring Data Analyst, to hiring Data Scientists, Data Engineers, Machine Learning/Artificial Intelligence Engineers, not because their is an actual strategy or objective, but just because everybody is doing it. Leading to companies spending exuberant amount of money, attracting and keeping data scientists, who spend most of their days, extracting, cleaning and modeling data, without knowing how it will solve an actual problem or create a new business opportunity that will generate revenues or profit.
What To Do?
So if you or someone in your organization just hired a Data Analyst, Scientist or Engineer, and you have been wondering if that was the right thing to do, I broke down the four steps, that I believe any organization either startup or enterprise should go through, if they want to succeed with Data.
Step 1: Assign a Head of Data or Chief Data Officer
In my opinion the most important decision Management/Founders have to make, is to make someone fully responsible for Data. A large part of this person’s responsibility will be company-wide management and use of data as an organizational/strategic asset. This means, working together with ever single department to design a common way to acquirer, store, manage, share and use data, but more important ensure that the culture adopts to a data-driven way of thinking and decision making, through facilitating conversation and sharing decisions and successes.
Step 2: Align the Organization’s key aspirations with the Data Value Chain
Every company goes through different stages in their Lifetime-Cycle, when transforming an organization into a data-driven one, the aspirations on the data value chain need to align with the organization’s primary needs. The first priority of the assigned Head of Data/Chief Data Officer, should be to determine what the company’s aspiration is, so they can align it with their strategy.
For example; Data Integrators primarily pursue implementation of a modern, integrated internal data infrastructure, whereas Business Optimizers focus primarily on exploiting the established data foundation to make internal and customer-centric business processes as effective and efficient as possible, and Market Innovators focus primarily on expanding cognitive capabilities to become digital disruptors.
Step 3: Develop a Strategy in line with the Organization’s key aspirations
As the company’s aspiration has become clear, the Head of Data/Chief Data Officer, needs to create a business-driven data and analytics strategy and develop a data-driven culture. This can range from establishing company-level, business-driven data and information governance for Data Integrators to establishing “fit-for-purpose” governance for the Business Optimizers and Market Innovators. However, even the best strategy can falter if the business culture is not willing to change. Data Integrators, who thrive on evidenced-based way of operating need to establish a data-driven culture, whereas Business Optimizers and Market Innovators, need to adopt a “fail-fast” agile software development culture to increase speed-to-market and innovation.
Step 4: Attract talent needed to fulfill your Strategy
Finally, to achieve the goals, that align with the aspirations and strategy that has been created, the Head of Data/Chief Data Officer needs to fill the roles that are needed to achieve the objectives. Data Integrators require more technical roles, such as data architects, analysts and integration specialist, because the internal foundation still needs to be established. Business Optimizers dependent on if they focus on expanding the ecosystem or on the analytic application of the data, require a hybrid between technical roles and mathematical/business roles like data scientists, data modelers and business analyst to drive optimization and innovation. Finally, Market Innovators require Data Scientists and Data Visualization Specialist to convey complex new concepts in understandable visualizations.
Become Data-Driven or Perish
I strongly believe that companies who don’t believe that their Data is an Asset and it should be managed accordingly, are going to be in a lot of trouble in the next five years. But instead of focusing on hiring a “Data Person”, I believe time and effort should be spend on getting someone in your organization, who is able to see the bigger picture and devise a strategy, has the people and communication skills to transform an organization and is willing and especially patient enough to be on the frontline to move a company from Data Integrator all the way down to Market Innovator.
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Sources/Additional Recommended Reading:
The Chief Data Officer Playbook by IBM Institute for Business Value
Three keys to building a data-driven strategy by McKinsey&Company
The Chief Data Officer: A Welcome new C-suite member in data-driven organizations by KPMG
The 5 Essential Components of a Data Strategy by SAS