Mark van Rijmenam is an entrepreneur, big data strategist, public speaker en author. We love to share his excellent tips with our Keytoe-audience. It’s helpful for everyone. Sorry for all Dutchies: this one is written in English.
Different ballgame
Knowing what Big Data is, is one; knowing what a Big Data strategy is and how the organization can benefit from it is two; knowing how to implement that Big Data strategy is a different ballgame. At least, that is how a lot of organizations perceive it. And it must be said; in large process-directed organizations it can be difficult. In fact, research has shown that 55% of Big Data projects fail. Developing a Big Data strategy involves an organization that really understands what Big Data it, what the (technological) requirements are and what the different possibilities are. So, how should you develop a successful Big Data strategy to outperform your peers?
1: Obtain Management Buy-in
Apart from having a clear understanding what Big Data is and how it can benefit your organization, you should obtain management buy-in from the start; preferably from board level, because a well-planned Big Data strategy affects the entire organization. Next to that, the results from Big Data projects could take longer than expected, require more investments and even may turn out negative in the beginning. Positive results will come, but could require some time. In fact, Big Data projects typically take up 18 months before they are finished. Management buy-in helps to these Big Data projects are not stopped before any real results can be shown.
2: Create a multi-disciplinary team
Data tends to remain in silos across the organization, with different owners of that data. Creating a Big Data project team with members from different departments ensures that no valuable data sources are left out and helps define Big Data use cases that really can contribute to your organization.
The marketing department should be involved because of the customer point of view. Involve product management to understand how data is gathered in the products or services offered. Involve human resources to apprehend the effect of data gathering on employees. Involve compliance and risk to guarantee that the privacy of your customers is protected. Include the finance department to keep the budget under control and of course involve IT to build the required hardware and software.
3. Start brainstorming and select a POC
Including all departments within the organization has a major advantage when defining the possible Big Data use cases; brainstorm sessions will become a lot better when people from different disciplines are involved. Each member of the multi-disciplinary Big Data team is able to offer a different point of view on data and together a large pool of possible use cases can be defined.
Once a few dozen possible use cases have been defined, it is time to develop criteria to rank all use cases. It will help to divide the use cases into different categories first, such as the use cases that fix bottlenecks within the operation or use cases that improve the efficiency of business processes. Use the criteria to rank all use cases in the different categories. Criteria can be the impact on IT, the impact to implement the solution and/or a possible value proposition.
Based on the criteria and the selected categories it is possible to select the Proof of Concepts that will be realized. The multi-disciplinary Big Data team should be able to realize the Proof of Concepts with minimal efforts. It is better to fail fast and fail often than to develop a complete solution and notice in the end that something was wrong. While Big Data has the potential to bring a lot of positive results, it is possible that this is not evident from the start. Don’t be afraid to fail and restart in this phase, as it is part of the learning curve how to deal with Big Data and to better understand how your organization can best benefit from it. For each organization after all, the benefits will differ.
4: Share the results with your organization
The moment the first results come in from the proof of concept, it is important to share the results immediately with the entire organization. Big Data does require a different culture, where all employees understand the implications of Big Data and will base their decisions on all available data. Sharing the lessons-learned from the Proof of Concepts will get the entire organization involved in Big Data and positively affects the required cultural change.
5: Extrapolate and scale up
If the results of the Proof of Concepts are positive, it is time to expand the multi-disciplinary Big Data team throughout the organization and to start more and larger projects. With the lessons learned, it will be possible to extrapolate the results of the first projects to the new projects and you will be able to better define a possible ROI, IT impact, possible process implications and other important criteria.
From there on, the entire process starts all over. For each new project that is done, it is of course important to fail less and implement faster, getting positive results faster. Research has shown that organizations that successfully leverage big data outperform their peers financially by 20%. So, whichever use case is chosen, in the end it will affect the net results of the organization positively, as long as the Big Data projects are implemented wisely and correctly.