Our approach to data has changed drastically in the last two decades. Not so long ago, it was viewed only as a by-product of business, and had no real value of its own. Today, however, organizations collect data from several disparate, fragmented sources, which is then analyzed to generate actionable insights and build a competitive advantage.
Though the scope of data has transformed, companies are yet to make it a core component of their business strategy. This leads to a fundamental problem — companies have a lot of information, but are unaware of how to utilize it to their benefit.
Companies must invest time in devising a data strategy that ensures all data resources are placed in a manner that they are utilized, shared and transferred in a simple and efficient way. A robust data strategy is key to keeping in check that data is used as an asset and not merely a by-product.
Most businesses today invest significant time and resources in defining and prioritizing their overall objectives, but fail to include data in this exercise. Lack of insights result in business teams being unsure about technological advances and their merits. Engineering teams, on the other hand, end up focusing solely on the technological tools without aligning them with business objectives.
That’s why, it’s important for your business and technology teams to be in sync and align the data strategy with the overall business priorities. At this stage, all teams need to identify and prioritize data points that will help achieve business objectives and define use cases accordingly.
Once use cases are defined, it is important to understand the type of data being used and the volume of data that will be generated in the immediate future. Small data volumes are faster to process and simpler to analyze. With large data volumes, technical architecture becomes more complex, which increases both costs and risks in implementation.
Hence, when it comes to data strategy, the focus should remain on three main aspects — data sources (and their staging area), data processing platform, and delivery solutions. A comprehensive data strategy will factor in all of these components, and also ascertain how they align with the business objectives.
As part of a long-term data strategy, certain short-term priorities can deliver immediate results. Ideally, identify around 1-3 data-based quick wins. These should be fast, relatively inexpensive measures that will showcase the Return on Investment (RoI) from data as soon as possible. For example, customer churn analysis helps reduce customer turnover and aids business efforts.
Inculcating quick wins is an excellent starter that leads to a more far-reaching data effort. It sets the tone for more investment and innovation in data management.
A robust data strategy will account for data governance to prevent liabilities. This must incorporate transparency, security, quality, privacy, and other ethical issues to ensure honest and open use of data across the organization. Data usage will also need to be minimized wherever possible, to ensure compliance with the fundamental principle of data governance.
Most companies fail to use their data because they lack the relevant skills within the organization. A good data strategy will help you utilize existing skill sets and figure out how to fill any gaps. This could mean upskilling in-house staff, hiring new talent, or outsourcing your data needs. A skilled workforce that can handle upcoming software and platforms is equal to a business that is always future-ready!
The purpose of any solid strategy creation tool is to allow the users to create a plan in order to ingest, standardize, and transform data efficiently — and that’s where DataBlaze comes in. A tool like this comes with multiple benefits for companies, including risk mitigation, quick identification of fraud, and efficient determination of tax exposure. Its effective strategy template allows users to drive data source onboarding and processing. Once the strategy is selected (with the required parameters in place), the user just needs to onboard the data sources. All subsequent activities — ingestion, pipeline creation, source to target mapping, scheduling and execution— will be done automatically by DataBlaze, in line with the strategy. If there are standardization/transformation activities used in the strategy, then the data processing pipeline will also be created automatically. Users simply need to input the processing logic. This will end up saving data engineers’ time and energy.
DataBlaze also enables easy monitoring and tweaking — the view page displays the strategy in the flow chart format, and if the user wants to make some changes, they can make it by simply clicking on the relevant flow chart box.
Once you have taken a look at each of these areas, you can create a more formal data strategy document. This will give you a bird's eye view of all your data requirements and priorities, and help you execute them optimally over the next few years.