Every company needs to define the strategy, policy, and procedures that will lead them to get the most profit from the valuable asset that data represents. We need to put in place a successful data strategy to manage the data adequately so we can grant that we have reliable and quality data for our analytical purposes and decision making.
But this is not always an easy task. Data are usually disparate, unconsolidated, unstructured, and even hidden. And all of them need to be managed as a critical asset for the company in a coherent strategy.
What we should define in our data strategy
For ensuring that our data strategy covers all the data lifecycle and will allow us to make the most profit from our data, this needs to include the following areas:
First of all, we need to design what the policies, procedures, roles, and responsibilities will be in our data strategy, and what are the technologies that we will use to that end. We need to define the framework that will rule our data strategy: who and how will be the guardian of our data and will be the "the chosen one" or ones for that purpose. Among the different things that we will need to define at this stage, it's included the detailed roadmap on how we are going to make sure that our data strategy accomplishes with all our regulations in terms of privacy, for example.
Another important thing that we will decide in our data strategy is our Data Architecture. We need to design a Data Architecture following the overall strategy and adapted to the final use that we want to do with our data. Our corporate Data Architecture needs to match the data model that we need for our business, so we need to define the configuration of our database or databases, how we are going to store data, the architecture of our metadata, the data model, the data integration, etc. This architecture needs to match with our current business needs but be flexible enough to adapt to future needs.
Every company has data privacy and data security needs. So we should have it in mind when designing our data strategy. Incorporating best practices for data security from the very beginning, from design, might help us to prevent security gaps and leaks. Data protection needs to define the accesses, roles, and permissions over data and databases, so we can avoid access by unauthorized users and prevent the inappropriate use of our corporate data.
As the end of our data will be decision making, we must be sure that the data we are using is quality data. To be used in decision making, our data needs to have integrity, reliability, consistency, completeness, and quality. And to ensure that our data has these attributes, we need to put in place different actions and techniques.
Where are we going to find our relevant data? How can we identify what our data sources are? First we will need to answer these questions and once we have clearly defined where we have data that can be valuable for our analytical purposes, we will need to see if this data is being already stored and where, and if not, look for the right technology to gather it and make it be part of our data streams.
And, in the end, we would need to have our disparate data, not just with the required quality, but also consolidated and integrated so that we can use a single and coherent source of data for our analytical purposes.
What are the keys to a successful data strategy?
Before designing a data strategy, we need to make sure that it is 100% aligned with business vision and goals.
Identify the main indicators
We would need to measure the success of our strategy, and to do so; we need to determine the main KPIs we will be tracking.
Draw a roadmap
It will be essential for us to design a project plan, with clear phases, milestones, and identified risks.
Involve final users
When we design our data strategy, we need to clarify what will be the ultimate use of the data and who will use it, and ideally involve this final user in the design of the strategy.
Types of Data Strategy
There are two main approaches a Chief Data Officer (CDO) can take towards the company data strategy: defensive and offensive. And you can choose one of these two approaches or combine both of them, which usually is the most convenient option.
To understand better what each strategy means, let's define each one:
Defensive data strategy has as a primary business objective the minimization of the downside risk, and their main activities are addressed to ensure compliance with regulations, use analytics to detect problems and build systems to prevent them.
Offensive data strategy bets for using data for increasing revenue, profitability and customer satisfaction, and all the activities are designed to be customer-focused and bring new and compelling insights to sales, marketing, innovation, etc. for better decision making.