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How to Boost Your Business with AI Feedback Loops

By 06.27.19
Reading time: 2 minutes

In the past few years, it’s become apparent that nearly every industry has begun reaping the benefits of data science. However, only a small minority of businesses are capitalizing on these breakthroughs. Why is it that the few rather than the many have been able to successfully realize an ROI on their data? The reasons are multifold, including:

  • An internal data science practice is expensive and difficult to organize
  • Companies may not have the data quality necessary to realize benefits
  • There is lower-hanging fruit to improve KPIs and business objectives

AI-driven insights and analytics

Given the high barrier to entry and lack of guaranteed results, many technology decision makers shy away from taking the plunge into the AI space. After all, as with other technology improvements, it’s better to have competitors invest in the development and reap the benefits of the mature product, isn’t it?

The mistake so many make with this assumption is that the AI implementation path is linear — install the product, configure the settings, capitalize on the advanced insights and analytics. While this may be true by happy coincidence in a limited number of use cases, the actual power of data lies in establishing a feedback loop between data and business goals.

Even if you have a firm understanding of the nature of AI and data science, it can be hard to know where to start or how to harness what you’ve already got. Building out your own team takes time and funds that would be better allocated elsewhere.

An experienced partner will help you understand how your data will feed the various business use cases that will give you the marketplace edge, how to make the most of your current data initiatives, and how to establish the data collection practices that will enable you to become a data-driven industry leader.

Building a baseline

Partnering with an experienced data science team you can trust allows you to establish your current AI-readiness baseline:

  1. How good is your current data quality?
  2. What KPIs can your data potentially drive?
  3. What data is missing in order to establish those metrics?
  4. What business processes need to change to best gather/respond to these data drivers?

Waiting until your competitors have gone through this process in several iterations will mean a significant lag in going to market with these data strategies — never a good thing. Your business may need to pivot significantly not just in terms of data collection, but in fundamental processes in order to stay competitive.

Einstein Data Discovery from Silverline

Silverline is ready to help you begin your journey; the first small step is crucial to a long-term successful data science practice. Our Einstein Data Discovery offering is a small, easy way to understand where your company stands in its data readiness for industry-specific use cases and will prepare you for future success.

If you’re interested in learning more about what Silverline can do for you, contact us.

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