How to build a corporate data strategy: The executive's guide
The way businesses perceive corporate data has evolved a lot in the last two decades.
Traditionally, corporate big data was seen simply as one facet of a technology project or one of the many externalities stemming from business activities or processes. Today, corporate data is one of the most valuable assets companies must manage. Big data is shared across several different systems and grouped together in order to allow for relevant, profitable insights.
Setting up a comprehensive, flexible big data strategy allows the company’s decision-makers to deal with all sort of issues and delivers decisive competitive advantage. Nonetheless, companies struggle to develop a corporate data strategy that fits their needs. Most businesses have not updated the way they capture, share and manage corporate data assets. They still approach it as a byproduct of their activities. And, as corporate data keeps expanding, IT costs continue to grow.
Most organizations still have not clear how to manage corporate data. Often, different projects end up managing their data independently due to the absence of a structure or protocol that enables distinct systems to share data, communicate and collaborate. This, in turn, generates higher costs and processing overlaps caused by the fact data is being duplicated.
Lacking of a comprehensive data strategy therefore is becoming increasingly risky and costly. Executives are required to change their approach to corporate data if they want to extract valuable information and optimize the way their company faces the many difficulties modern organizations are confronted with.
Corporate data strategy and competitive advantage
Big data has an amazing potential. However, many big data strategies don’t bring the expected results because investments are focused on each area of the company separately. Such lack of coordination and alignment is the root of many data access and usage issues.
Setting up a corporate data strategy prevents this from happening. By setting S.M.A.R.T. goals within each area, it brings clarity into the relationship each department have with one another. Further, it sets a company standard for how to approach and practice data management, manipulation and sharing.
Overall, the main advantage of implementing a corporate data strategy is it establishes a common road map guaranteeing that all data-related activities, such as data governance, integration or data quality projects, just to name a few, are complementary and coordinated. It makes cooperation easier and ensures data is used in a more efficient and effective way.
What is a corporate data strategy?
Shaping a well-tailored corporate data strategy requires a clear understanding of what that is. In plain language, developing a big data corporate strategy means organizing all corporate data resources so that they can be used, shared and moved quickly and efficiently. It means ensuring corporate data is treated like a core asset and that the organization adopts the right tools.
There are four key elements that define any corporate data strategy:
Corporate data platform: i.e. an IT solution that combines the attributes and capabilities of several big data applications and utilities within a single solution. There is a wide range of options you can choose from, such as on-premise technologies, cloud infrastructures, Software-as-a-Service (SaaS) platforms and hybrid solutions. Your choice should be based on your organizational goals and needs;
Big data analytics software: i.e., a sophisticated software that analyzes huge amounts of data and presents the relevant information it extracted from such data. When choosing which big data analytics solution fits you, make sure to approach big data with a result driven mindset and focus on the value of your data, rather than on its volume. This will help you when presenting the results of your big data project to your stakeholders.
Data integration solution: a vast number of data integration tools, softwares and solutions is available on the market. Corporate data is often siloed and needs to be integrated with other data that is either siloed either coming from third-party. Data integration generally provides insights that otherwise would not have emerged.
Data security solution: valuable corporate assets need to be protected. This is the case with corporate data as well. Creating and updating policies promoting the responsible collection, retention, and use of data can help you preventing that any piece of critical corporate information gets shared outside of the boundaries of the organization. Further, make sure you trust both your big data solution provider and the people who build and maintain it.
One common mistake when defining a corporate data strategy is to consider it a one-time thing. Developing a data strategy is an ongoing, never-ending process. Taking a static approach to your data strategy will only deepen the issues your strategy was meant to solve, such as resource shortfalls, technical gaps, and missed opportunities.
Developing a corporate data strategy
A successful big data strategy focuses on facilitating cooperation and promoting an efficient and effective use of the company’s data assets.
The main challenge when developing corporate data strategies is to provide easy access to data at any level of the organizational chart. Indeed, difficult data access hinders data usage, leading to the failure of the strategy.
One way to overcome or limit this challenge is to assess your business needs and goals before developing your corporate data roadmap. Identifying at an early stage the strengths and weaknesses of your data environment will help you mitigating risks, prioritizing your return on investment and boosting data access and sharing.
A comprehensive corporate data strategy must address the way data is identified, stored, shared, processed and managed. What follows is a description of each of these five defining elements of a corporate data strategy. It is based on the report "Supporting GDPR in a Data Driven Organization" published by SAS Global Data Management.
1 - Identify
Generally speaking, data can be either structured or unstructured. It can originate from several different sources and can be located in a lot of different spots. Despite all the characteristics of the data you are dealing with, your data strategy should help you identify relevant data and make sense out of it.
As the name says, big data involves a huge amount of data. Such data is generally characterized by a high degree of diversity, which can be an obstacle when manipulating and processing data. A good practice to overcome this issue is to formulate internal standards to help employees share the same approach and terminology when choosing data name, format and representation.
Metadata covers an essential role in ensuring corporate data can be accessed and used by any employee. Make sure to develop a consistent data glossary that helps identifying and organizing data across your organization.
2 - Store
Data storage can be a demanding process. Data should be stored in a structure and location that smooth data access and processing across the organization.
As organizations collect and share among them an ever-growing amount of data, it’s becoming gruelling and impractical to store all corporate data in just one location. That would be easy if data always came with the same structure, format or any other defining feature. We know it never goes this way.
In short, don’t worry if your corporate data isn’t stored all in the same place. Just make sure your data storage solution allows everyone to easily access the information they might need or want to use. Kind of like a library, which has many rooms but is organized to help people find what they are looking for.
Currently, though, many organizations focus their whole strategy on data storage. While storage is a critical aspect of any big data strategy, this approach to data strategy doesn’t ensure your organization will develop better procedures to collect, manage, share and use data. Remember to focus on all the five elements mentioned in this post and not to concentrate all your efforts on what is just one facet of your corporate data strategy.
3 - Process
Corporate data can be classified as either internally or externally originated. Internal data comes from the company’s application systems; external data can come from a large number of sources, none of which is owned by the company. It might originate from government, business partners, data providers…
Regardless of its origins, data is initially “raw” and not ready to use. It needs to be processed, i.e. prepared or transformed so that it can be readily used. In other words, you need to integrate your corporate data in order to provide a unified, consistent data view.
Move and combine your corporate data residing in disparate systems in order to eventually create a small set of homogeneous data sets that can be easily merged or integrated by any data user based on his specific goals or needs.
Successfully processing corporate data requires companies to provide their employees with the right tools and processes so they can easily use the data they need without requesting any IT intervention.
4 - Package and share
Originally, application systems were not designed to share data. There were only few systems and only a very restricted number of tech-savvy employees needed access, so companies didn’t bother packaging their data in a way that facilitated sharing and reusing data. Data was packaged based on the developer’s preferences, rather than with the final data user in mind.
Today, companies are required to share more and more data among a large number of systems. Sharing data can no longer be considered a technical skill of software architects and developers. Companies that will fail at efficiently and effectively sharing their data within their boundaries will face production and operation challenges that might even put at risk the survival of the business. It has become necessary for any business to set some standards procedures on how to package and share data across the organization.
5 - Govern
As data used to be considered as a business externality, it was not actively managed. However, as the way we perceive both data and externalities has changed, companies now need to establish new mechanisms to ensure corporate data is consistently managed, manipulated and accessed across all the organization.
Data governance consists in establishing and communicating information policies and mechanisms for effective and consistent data usage. Corporate data policies can target data security requirements, correction logic, naming and quality standards, or even how to use data.Together, these policies reinforce one another and ensure data is easily accessible, usable and shareable by any staff member and not, as it used to be, just by the application developers.
The main challenge in corporate data governance refers to its adoption. Data governance should be approached rigorously. However, make sure the set of policies your organization adopts is not too overwhelming or demanding in order to facilitate its implementation.
In conclusion, corporate data strategy is a critical need of modern organizations. Those who will be able to create a comprehensive, sound data strategy will reap great benefits, both in the short and in the long term. They will find themself in the right position to respond to the evolution of their needs and of the context surrounding them.
However, data strategies must include the 5 critical aforementioned elements and be constantly reviewed. Focusing on one specific elements or approaching data strategy as a one-off activity will eventually lead to failure. Of both your data project and your business.