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  February 5th, 2018 | Written by

How to Assess Your Data Strategy for Improved and Advanced Analytics

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  • The debate about whether artificial intelligence is real is over.
  • Cloud storage is the future of data warehousing, but not everyone has made the switch yet.
  • Timely business insights through predictive analytics is one of the most useful AI tools available.

As IBM CFO Martin Schroeter said recently, “The debate about whether artificial intelligence is real is over, and we’re getting to work to solve real business problems.”

As a CTO or executive, you know that if you’re not adopting new data-driven solutions, you’re losing your competitive edge. Yet, it’s challenging to know exactly where some of the new data tools, such as artificial intelligence (AI) and data science, can be applied. Then, how should you roll it out?

Gartner emphasized recently that businesses need to make steps to modernize or they’ll be left in the dust. Now that leadership is paying attention to data science and AI, with 64 percent of senior decision makers believing their organization’s future growth is dependent on AI technologies, it’s important to clear directives so business decisions can be guided by data. Getting started with artificial intelligence and data science means keeping the focus on where you can maximize data efforts for achieving market dominance. The following questions will help any business access the current state of its data strategy for implementing advanced solutions.

Where is my data coming from? To get the most out of the artificial intelligence movement, it’s critical to apply it to a specific problem. Start by identifying every method you currently have of collecting information. Essentially, anything that measures how your company is performing and how your stakeholders are interacting with you.

How is my data stored? It’s important to understand your needs and your options for data storage. Cloud storage is the future of data warehousing, but not everyone has made the switch yet. Some companies have in-house systems they’ve invested heavily in, and uploading everything to the cloud doesn’t make sense for them. There are also hybrid warehouses where some incoming data flows get tagged for in-house storage while others are directed to the cloud.

Who has access? On the surface level, this one is simple: Who can see your data? What are the security procedures used to keep unauthorized users from using/altering data? There’s an executive trend towards democratizing data so that it’s accessible by every department. That provides an incredible amount of flexibility and encourages innovation on an individual level, but there are some security concerns involved. Decide what level of access each category of employee will have based on what you deem an acceptable balance of risk.

How is your data being used? Clearly define what you want your data to do and the goals you are working towards in gathering it. While these will, by nature, be loosely defined, try to narrow it down more than “growth”. A better exploitation goal would be “increase growth in X market” or “improve the customer acquisition funnel”. Be specific about the information you need to track to attain this goal, but don’t feel the need to ration it. Data science needs data to work. The more relevant information you have, the more ROI you can realize from data science programs.

Gathering Timely Business Insights From Data

Timely business insights through predictive analytics is one of the most useful AI tools available. Information about equipment function, fleets, inventory and more can be analyzed in real time and presented in a format accessible to the non-technically inclined. Accessibility is a huge step forward for analytics, considering that the old model involved waiting for a specialist to translate data into usable graphs. Two years ago, only 51 percent of decision makers felt they could interpret their enterprise analytics without assistance. Now, that number is 66 percent and rising. Having analytics that executives can forecast boosts flexibility and allows departments to make changes before it can affect business operations.

In following this roadmap, you’ll not only identify the true extent of any problems within your current strategy if one exists, but quickly see how dialing in on a more specific direction for your data strategy will remove conflicting data streams and smooth out wrinkles. When it comes to advanced analytics, incorporating data science and AI into existing workflows is most efficiently done on a rolling phased basis. That is, identify the first few steps to improving your data usage, then periodically reassess and add new steps as the old ones are completed.

Humberto Farias, CEO of Concepta, is a seasoned technology professional with over 18 years of experience guiding companies around the world through the custom software development process. He leads a team of highly skilled software engineers and developers providing tailored web and mobile applications to enterprises.