As businesses have started to realize the potential insights they could be gleaning from their data, and the absolute necessity of using it to their advantage, they’ve also realized that harnessing it and making it usable can be daunting. But most recognize that data has to be a priority to remain relevant. So, where is a good place to start when embarking down the get-insights-from-my-data path? Here are some basic steps we follow as we help our customers tackle their data…
Step 1 – Define what you’re trying to achieve.
Before data even comes into the conversation, it’s critical to determine where are you trying to go. It does no good to embark on a data modernization initiative until you determine what your end state is - what are you trying to accomplish with your data? Some organizations are just looking for a way to capture the data more efficiently, where others actually want to take it to the point of using it, to analyze it and drive their business forward. Defining your end goal will ensure you don’t waste time and money on a pointless quest.
Step 2 – Understand and document what data you have and where it is ingested.
The majority of organizations don’t even know what data they have, as it lives all over their environment in various data silos. This is where due diligence comes in, understanding and documenting the who, what, when and where of your data.
Step 3 – Ingest and consolidate your data.
Once you’ve figured out where your data resides, it’s time to transfer it from those disparate systems to a single target destination. You must transform unstructured and semi-structured data via an ETL/ELT (extract, transform, load/extract, load, transform) process into usable formats, enabling a continuous flow of updated data that combats data decay. Effective analytics requires clean data, so this is critical in any data initiative – without accurate data feeding into the analytics tools, the value is greatly diminished.
Step 4 – Train and model your data.
AI and machine learning are two of the most impactful technology trends in generations, and really where data starts turning into insight. Define AI/ML models and desired outcomes, and then determine and ensure you have enough compute capacity to employ those models, and whether they should be in the cloud or on-premises.
Step 5 – Visualize, analyze and serve up the data.
Provide the organization with relevant analytics and data visualization, along with self-service analytics for daily line-of-business and departmental uses. There are many tools to accomplish this, depending on your specific environment and goals, making an experienced partner a strategic asset in determining the right-fit tool(s).
The bottom line.
When it comes down to it, the really critical part of any data initiative is… rather than taking a system-centric approach to data, take a step back and take a business-centric approach. Define what you’re trying to achieve, and then use your data to get you there. Data modernization is more than just buying a bunch of data management tools. It’s much more a strategy and methodology than it is a shopping list.
The right methodology for data modernization starts with the end goal in mind. When you know what you want to get out of your data, then you can work with an IT services partner to evaluate your existing tech stack, personnel and in-house skills to discover what needs to evolve and which new tools should be added.
Interested in learning more? Check out this informative eBook on why data modernization is key to unlocking business opportunities – and how to get it right.