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What is a Data Strategy? It’s Importance and key Elements
What is a Data Strategy?
Data strategy in simple words is the framework or roadmap that allows us to draw important business insights from the data around the mission and vision statement of the company. Based on which organizations gets the solution of important business questions and gets the direction in which they should move forward. But getting insights and solution is only the fruits of the tree, on roots level a data strategy defines how the data is going to be collected, stored, used, managed and shared in an organization or a company.
Blog Content At A Glance
Data nowadays is not just a by-product of the projects or processes in a company but is, one of the most valuable resource for any organization. Data is extracted, stored, used and processed to get useful business insights. It is a leading factor for continuous improvisation and analysis of the product or service that the organization provides. It provides a key role in the growth of an organization, giving an edge in the competition if done well.
Data should be used and managed like an asset of the company, as it provides key factor for processing and decision making. A data strategy sets common goals and objectives across projects to ensure that data use and management is both effective and efficient. It establishes common methods and practices to manage, share and manipulate the data across the organization in a repeatable manner.
Data Strategy is always a custom-built solution for every organization. Some common type of data strategies are Federal data strategy and Enterprise data strategy, which just denotes the usage purpose of that strategy. Federal data strategy is a data strategy which is built for federal agencies which is usually a 10-year plan, here the privacy and governance is prioritized over the efficiency whereas in Enterprise things are quite balanced as discussed here.
Now, some may ask why we need a Data Strategy at all? If things are already going well, so why to bother?
Do we really need a Data Strategy?
Understanding the need for a data strategy is the first step into building one, here are some important aspects highlighting the need of a data strategy that we have stated here as:
1. Increase in Data Volume
Nowadays every business process, IOT devices and many other things are generating data every second. The Importance of data is increasing like never before because data driven insights are now the prime source of continuous improvisation of the business processes and direction. Increasing volumes of the data day by day is making the data management more cumbersome and almost impossible for an organization to keep track of the relevant data. This is the reason why a data strategy is also referred to as a Big Data strategy.
2. Data Quality
As already discussed, due to the improper management of the humongous data, when some relevant data is needed then the amount of time and resources consumed to get it makes the entire process slow and inefficient. Not only it makes the accessibility poor but also due to the lack of standardization of the data leads to poor data accuracy and creating lot of issues related to data quality.
3. Data Security and Governance
One of the most the important reason for a well-developed data strategy is the increasing rate of cybercrimes and thefts, due to which even well-established organizations and MNCs are facing humongous losses world-wide, which makes the Data governance to be as essential as a salt in a dish while making your data strategy. Otherwise, your company can face some serious legal issues with data privacy of your clients, as we have seen in past with Facebook and Google.
4. Collaborative Efficiency
Another major impact of not using good data strategy and just following the conventional method of approaching the data just as a by-product is going to make the different projects in the organization less efficient. As all activities addressing the data needs in various projects will be independent from one another without the awareness of overlapping efforts and costs. By using a well-developed data strategy different projects could cross collaborate with each other and make the overall process efficient and seamless.
What are the Goals of a Data Strategy?
- Using data to get the Insights or it is also sometimes referred to as data analytics strategy, which includes things like
- To get the complete overview of customers
- To know about internal progress of the company
- Future predictions of the products or services
- Offering smarter and more customized product or experience to the customer
- To improve your internal processes, collaborations and operations of different projects in the organization
- To make or improvise the data management system, ensuring that Data is of high quality and is also highly available (Which is the part of data quality strategy)
- Including privacy and security of the data.
- Monetization aspects of the data: packaging and selling it to some potential buyers
How to achieve the Data Strategy Goal? Components of the Data strategy
- Build a suitable Data Governance system based on the need of the organization and sensitivity of data, as making too many rules and regulations where it’s not needed will make the system inefficient.
- Build a suitable Master Data Management system or data management strategy to refine the data quality, the better your data quality the better will be solution delivery of your data strategy.
- Technology and Architecture: Find the Right tools and architecture required for the applications that we are going to use in the Data Pipeline.
- Data Strategy Roadmap: Time horizon plan of how we will manage, facilitate and communicate the changes that we are going to make in the business.
What are the Key Elements of a Data Strategy?
1. Business Alignment:
Define your overall business strategy and align your Data strategy to it. A data strategy has no meaning if we don’t have the business strategy aligned with it, data strategy is always built for the support of the overall business strategy and goals. It should clearly define how a data is going to address the specific needs of the business and generate the value and return on investment. Aligning means answers to these four questions should be coherent to each other:
- Why does your organization exist? It also includes the following points
- State deliverables and target personas: what you going to provide and to whom
- Define your core competencies: what is your unique signature Identity in business
- What insights you need to measure and direct your progress in the market?
- How can a data strategy help you get those insights?
2. Sourcing and Gathering:
This means what type of data you need (like it may be structured, unstructured or semi structured or may even be a combination of them, as per the needs of the insights and predictive model we are aiming for) in your data strategy and from where you have to source that data (It may be available as inhouse data or even external source data if required) and having a proper method for their extraction based on its availability.
After the sourcing is done the next thing, we have to see is the gathering of the data, depending on the requirements we have to specify or select the single or combination of storage tech required for it. Like if we mainly deal with structured data then we should go for the Data warehouses as they will provide much faster query results and insights for the data and if we deal with multiple type of data types then we might have to select the combination of the storage type for their transformation and usage.
3. Technology and Infrastructure order:
In this we look for the tools, Infrastructure and tech that is required in execution of the data strategy. There are different tools and tech that are required as per the data strategy demands and execution which are to be provided for optimal execution of the strategy and avoiding the problems that arises with its improper execution (Like processing the query results directly on the Data base without a data warehouse will make the data base slow, which will cause a lot of problems to those who are dealing with it).
4. Data Insights:
Do the insights we are aiming for are specific and optimal as per the demands, the better analytics, insights and prediction we have for the overall business strategy the faster and better will be the result delivery. This aspect looks for the proper data visualization, using visualization tools which should not only make the analytics look good but also easy to diagnose.
5. Skills and Capacity:
This aspect represents the people and processes involved in the data strategy, i.e., which skill set is needed for the strategy implementation and do we have in house people with that skills and capability for it or hire some people or company for the same. Here we also look into the leadership role of the strategy, which means who will be Responsible of tracking and making the business changes and even in the strategy if needed (Generally are CDO).
6. Data Governance and Management:
Data governance is the aspect which makes is possible for sharing the data on enterprise level and proper management takes care for its being effective and efficient. The better synchronization of the two we have the better security and productivity we have.
It is the sum total of what all we have described in the time horizon format, it shows an action plan for whatever we have decided to do in the strategy. Apart from the timeline and actions there is also a scope for the change in it because a data strategy should also be flexible to changes that will come along the way.
Common Mistake that Organizations make
Of all the companies using data strategies only 10% are able to deliver on it. the problem is that the data strategies are not very well Developed, Understood and Communicated in an organization, so here comes the need of a well-developed Data strategy framework/roadmap, which gives us the blueprint of the action plans that are needed to be executed in each brief time period to achieve the Mission in a long run. Its proper communication is as important as making the data strategy itself.
Key take away points
- Data strategy is a user manual for your data across your organization, defining it’s every steps of usage along with the terms and condition that you make for it.
- Aim of the Data strategy is not just to have best management of data but to have Data Driven business insights, which helps you to solve the important business decisions and questions.
- Lack of a data strategy will make your organization slow, inefficient, less visionary and can even make your organization land on facing legal issues related to the data privacy.
- Main reason for a data strategy failure is that they are not well developed, understood and communicated in the organization.
- Clearly defining your Data Strategy goal and how to achieve that goal in the prescribed bullet points given.