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Using AI to Boost the Power of Your Analytics

Many organizations are using analytical tools and processes to generate deeper insights into critical aspects of their business. Manufacturing performance, customer engagement, marketing effectiveness, and financial projections are just a few ways in which analytics are being used to improve performance.

The advent of AI-powered analytics is now providing enterprises with the means to generate even more value from their data. Analytic tools supported by AI (Artificial Intelligence) provide capabilities that can move data analysis beyond the boundaries of data snapshots and into the realm of “learning” and predictive analytics.

AI-powered analytics enables the development of a “smart” analytics ecosystem that can fundamentally change the ways in which data is consumed, synthesized, and acted upon. This paradigm shift brings several tangible advantages, including the following:

  • Less reliance on human intervention/analysis
  • Transition from data reaction to data projection
  • Prediction of potential future business outcomes based on scenario-analysis
  • Automatic generation of insights and recommended actions
  • An analytics ecosystem that learns from the data that it produces

Use Cases – Benefits of AI-Powered Analytics

Getting Deeper, More Rapid, Insights from Your Data

Dig much deeper into your data than previously possible. Significantly increase the speed in which large volumes of data can be digested and synthesized. AI automatically uncovers hidden patterns that may have been too obscure for traditional tools to detect.

Ability to Ask Questions of Your Data Using Vocal Queries

AI provides the ability to ask questions of your data without having to be a data scientist. Use vocal queries to generate actionable insights from your data. Easily search vast volumes of data to extract specific sets of data. Quickly conduct user-friendly “what-if” scenarios that produce “smart” responses.

More Efficient Consolidation of Data

AI makes it much easier to consolidate data across disparate channels and platforms. This unified data can then be viewed in a single pane-of-glass that is much easier for the viewer to consume. This helps eliminate the human error and inefficiency than can occur from having to view data from too many data sources.

Predictive Analytics that Drive Proactive Actions

Predictive Analytics

The real power of AI lies in its ability to learn from the massive amounts of data generated throughout a typical business enterprise. That learning can be utilized to predict outcomes. AI enables the enactment of “what-if” scenarios that produce actionable insights. AI-powered analytics delivers unprecedented levels of visibility into the metrics that drive the success of your business. The ability to predict customer behaviors and preferences will enable you to better align product and service development to meet the predicted needs of those customers.

Cross-Channel Sharing of Data and Insights

An AI-powered analytics ecosystem will include the engine for the sharing of data and insights. The ability to share information via real-time dashboards, API’s, and embedded applications means that you are putting critical data into the hands of those stakeholders responsible for maximizing the value of that data. AI helps ensure that you are sharing the insights that matter the most and helps declutter your data ecosystem.

AI Adoption – Have a Plan

The transition from traditional analytics to an AI-powered analytics program will require some detailed planning and preparation. Several factors need to be considered to ensure that you fully maximize AI’s potential. A few things to consider….

Start Small and Build Up

Your AI integration will be a journey. Start with a few small use cases and develop a plan to apply AI methodologies to those restricted cases. Select use cases that would be the easiest to implement, measure, and validate. Ideally, these initial use cases should be tied to tier-2 organizational goals to allow for the learning process. Avoid the mistake of trying to do too much too soon.

Focus on What Matters Most

Upon success of your initial use cases expand your program to more high-impact areas of your operation. Build use cases that would directly impact your tier-1 strategic goals and objectives. These should be areas were AI-powered analytics would have a demonstrable effect on business outcomes (market growth, customer retention, marketing efficiencies, product placement, financial results, etc.).

Build Momentum with Success

Nothing builds—and sustains—momentum like success. Take advantage of early success. Publicize those successes throughout your organization. Demonstrate—in detail—how the use of AI made those wins possible. AI-powered analytics can significantly reduce the burden of repetitive tasks for your employees. The automation of data analytics, and the active learning from that data, will empower your employees to concentrate their efforts on those activities that are predicted to pay the highest dividend.

Emphasize Adoption

Like any fundamental shift in technology there will be those individuals that are averse to change. When not planned for properly this can completely derail your AI integration effort. Regardless of the power of AI, its impact on your business will be negligible if folks aren’t using it. Here a few things you can do during your transition process to help achieve cross-functional buy-in and adoption.

  • Involve your users from the beginning: Avoid the mistake of restricting your transition activities to only your IT staff. Get your eventual users involved in the process as early as possible. Involvement will breed enthusiasm and buy-in. Solicit feedback from potential users as you move through the process.
  • Have a realistic implementation schedule: Don’t rush the process. Create an implementation plan that is flexible and that allows for reasonable iteration. Provide enough space between project phases to allow users to fully digest the changes and to experience their benefits.
  • Training and Reinforcement: Your AI program will not be static. It will continue to evolve and expand. This will require a robust training program to keep users and stakeholders fully apprised of new capabilities. Consistently reinforce the importance of user adoption and feedback. Create internal success stories that highlight how AI has empowered your employees to use smart analytics to make smarter, more impactful, decisions.

 

You have likely invested much time, money, and resources in building up a traditional data analytics capability. Taking the next step by implementing an AI-powered analytics program will allow you to fully capitalize on the intrinsic value of that data by automating the process by which that data generates learning and insights.

Also read How Digital Transformation Energizes Project Management

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