Conversations across industries all reveal a common theme: businesses often struggle to get value out of the data in their operational systems. They’re using a range of platforms, but they’re not seeing the efficiencies or impact that they should. Enter a data analytics strategy.
Simply put, a data analytics strategy is an overall plan that gets you from what you need to accomplish to a plan that’s both repeatable and sustainable. We’ve had many conversations with clients who have 4-5 full time employees whose job it is to pull data and make reports. Technically speaking, that is a data strategy. It’s just not a mature one. The goal is to instead get a data analytics strategy that increases the maturity of how you interact with your data – so that you can be both proactive and efficient.
One of the biggest barriers that we see is that people are often intimidated about the process of creating a data analytics strategy. How we think about our solution is actually rooted in identifying that hesitancy – this can’t be a complex 50-100 page document. It’s supposed to be a few pages that explains what you want to accomplish, how your data supports your business outcomes, and the steps you’re going to take to align your data with those business outcomes to drive the company forward.
So why does this matter, and why is this no longer a nice to have?
- We’re living in the age of information, and you can be beat by data driven companies
Data is growing at an exponential rate – much faster than it can be worked with or have any value drawn out of it. The companies who are finding out how to tap into their data are winning. This is a new competitive advantage. Should I open another store? Should I go into a new product line? Should I ship more inventory to a store? The days of making business decisions based on ‘gut’ and without data, where you have no idea how impactful those decisions will be, simply are over. This is how we utilize data for true data driven decision making which is a core tenant of any solid data strategy.
- Human error and errors within the data are hurting your business
A part of your data analytics strategy is ensuring that you have quality data. If it’s being assembled manually, there is going to be human error. If you’re pulling from a source and that source has known deficiencies, there is going to be technical error. A data analytics strategy addresses all of these things – and the quality of the data is what allows you to deliver the right data to the right people at the right time and in the right format.
- To be successful you need to treat data like a program not a project living between IT and the Business
You need to know how to use your data. On top of that you need to have quality data to use. This said, making use of your data can be challenging and will ultimately lead to failure if not presented in the correct way. Treating data as a project tries to tackle too much all at once. The data program that implements the data strategy stores data from the source, curates it into values and terms that resonates with the business, ensures the quality of the information in those business rules, and enables the corporate strategy while also continuously evolving all of the above practices. This is where the data strategy brings true value and can drive ROI.
- Data is an evolutionary journey, and you need to start now
Enter it at any point, but once you enter it, you’re on a path. What’s at the end is that you go from being descriptive to prescriptive and you create a data support system that works in real time that can help you make decisions. Your data strategy is your future runway for the growth and scale of the business and where you’re headed – and can move you from a baseline of simply making better decisions (a great place to start) to a future-state of artificial intelligence and machine learning where you are able to truly transform how your business functions.
Wherever you are on your journey, AllCloud can help you build a data analytics strategy that 1) starts with the right foundation, 2) helps you define business use cases, and 3) lets you find value in your data that you never thought possible. Contact our experts to get started today!