In part 2 of this series we explored the importance of data lakes. With an established data lake, blending that data together in workflows requires analytics tooling. That tooling could include hiring data engineers and data scientists that can code the solution in any number of languages like Python or R. But if you don’t have those kinds of skilled resources on staff already, planning, managing, recruiting and retaining a team like that is a major endeavor. That is assuming someone in your organization is prepared to lead them properly. Some tools exist that don’t require scripting or programming skills, but all tools require diligence with your data and a willingness to spend time cleaning it and getting it in order. In part 3 of our Myths series we explain how laborious ensuring data quality and intelligence can be and how Blacklight Solutions helps businesses blend and enrich data without having to build a dedicated team or utilize extensive resources.
The Art and Labor of Ensuring Data Quality
Data quality will almost always be a serious undertaking. That investment in data quality is so high, it is one of the key reasons we recommend you start with business objectives and questions rather than starting with your data and getting it in order first. If you know the question you are trying to answer, you have stacked the deck in your favor when it comes to investing time and effort in the tedious exercise of cleaning and organizing your data. This is the stage where custom predictive models can be developed to do forecasting. Data can be transformed, reformatted and blended. All these processes once developed will need to compute power to run them. That might be extensive or simply a machine that can kick off a nightly process.
At each layer of data refinement, new security concerns will arise. Once data is securely in your data lake you only want suitable processes to be able to access it and to write it out. That could mean your data blending processes have the authority to decrypt data that is encrypted in storage. They may also require the authority to share it to other locations and consequently move the data internally or externally. It also might mean that data that was brought in from a source with constraints around being shared needs to be reformatted and written somewhere else, but still needs to maintain the original constraint. An example of this is when data is pulled from a social media platform that has license terms that prevent that data from being shared publicly. That data must be managed through point to point processing so that it is still constrained from public sharing wherever it is stored and whatever other data is mixed into it. The security concerns in the data processing and intelligence layer are just as important as those in the ingestion and in the following presentation phase.
The Blacklight Solution
We know this can be an overwhelming, time consuming endeavor. That is why Blacklight Solutions commits to a beginning to end approach for your business analytics needs. Our experts are with you every step of the way. By utilizing our expertise and Blacklight software your business can harness the full power of your raw data while we take on the labor of ensuring data quality and security. Blacklight Solutions takes the complex world of business analytics and simplifies it to help you derive intelligence. In part 4 of The Myths of Business Analytics series we will discuss a crucial step for a successful business analytics solution – presentation.
Click here if you’d like to talk to one of our experts on how to get started!