While this may sound obvious, true success largely depends on the quality of planning, implementation and execution that goes into creating a strategic plan. This remains true whether you are baking a pie, tackling an important home project or devising a new data strategy for your business. Failing in either of the planning, implementation or execution stages is a sure way to guarantee a suboptimal result, and can be the difference between a blue-ribbon masterpiece or a scorched waste of time.
The path to success is not always straightforward or guaranteed. In devising a successful data strategy, “the best” is a function of particular needs, goals and circumstances specific for every organization. There is simply no single solution that fits everyone. Nevertheless, there are methodologies which can practically guarantee that your planning, implementation and execution are better aligned with the particular goals and circumstances of the company, whilst retaining enough flexibility to accommodate changing circumstances the business may be faced with later on.
Before we get into the Four Pillars of a successful data strategy, there are some more general concerns we ought to address that don’t fall neatly into a specific category. Failing to consider these may be just as detrimental to your data strategy as neglecting the planning, implementation and execution stages described above. In particular, the three foundational considerations one should keep in mind when contemplating the rest of their data strategy are:
- Understanding the scope and impact of moving towards a data-driven business model;
- Detailed knowledge on how data is produced and where data comes from;
- Realizing the importance of building knowledge through effective concept proofing.
Scope and impact
A common shortcoming we often see with companies formulating a data strategy is when the undertaking is viewed merely as an extension of their engineering or IT projects. While those departments will indeed play an important role in developing and implementing a successful data strategy, the scope and impact of a thoughtful and comprehensive data strategy will normally extend well beyond those departments.
In order for a data strategy to be effective, companies often need to rethink the roles of people and technology as well. To be a data champion, one needs to recognize that virtually the entire organization, to some degree, will be involved in the generation, utilization and analysis of data. No longer can data be considered the domain of a single department and hence people and technology need to be aligned accordingly. The way the company operates now may be quite different from the way the company will be operating after the implementation of the new data strategy and the organization should be fully ready for this.
In forming your company’s data strategy, having a keen understanding of what data is needed and how it is produced is of paramount importance. The organization should acknowledge that there is no single data set that will fit the needs of every company and that certain kinds of data could be produced through multiple avenues. Fortunately, these issues can be readily addressed after considering your company’s goals, expected outcomes, some of the technologies involved and other key factors.
In later articles we will go into more depth on data and data production, but - for the purposes of this article - we can simply say that data is produced whenever content is stored in a system or when previously stored data is manipulated (mostly - but not necessarily - for analytic purposes). Data may be coming from a myriad of sources such as web pages, applications, IOT and other sensors, spreadsheets or analyses, etc. Given the seemingly unending range of data sources and formats, the efforts of many companies to combine them in a way that properly aligns with their goals and expected outcomes often fall outside of their core competencies.
We at DataMatrix can help in determining what data will be needed and how it should be stored. We can also lend assistance in considering what tools, technologies and talent may be required to gather and analyze that data in a way that aligns with the company’s goals. Without a thorough understanding of how, by whom, or from what your organization’s data is produced, stored and consumed, even the best data strategy is likely to fall short of your organization’s goals and expected outcomes.
Proofs of Concept (POC)
Having heard of the many promises and advantages big data and cloud claim to offer, you may have done some research, put a plan together and are now ready to pull the trigger on a sweeping overhaul and redesign of your company’s data strategy. Naturally, you will want this to happen as quickly as possible in an effort to begin capitalizing on the insights and opportunities that big data and the cloud can offer.
Unfortunately, this approach doesn’t always work as expected. As indicated earlier, moving towards a data-driven business model entails a myriad of potential changes in how your company currently operates. Regrettably, making these changes is not always a smooth process and any problems that arise will need to be dealt with in order for the implementation of your data strategy to successfully move forward. Hence rather than taking an all-at-once approach, we often recommend starting by implementing one or two strategic changes that bridge the most important gaps and promise the greatest returns.
Doing so will allow your company to begin reaping some of the benefits without necessarily having to make a huge investment. Well chosen POC’s will not only help to foster buy-in from the “data-doubters”, but will also build your employees’ knowledge base and decrease the steepness of the learning curve that comes from changing too much too fast. As such, an incremental adoption of your data strategy through the use of effective POCs, can help to mitigate risks and decrease transitional friction, thus allowing your organization to continue running smoothly throughout the process.
This concludes the overview of the foundational considerations. In the next section, we will look at the Four Pillars of a Successful Data Strategy themselves.