Dipping your toe in the AI waters?

First start by implementing a Data Fabric…

By: Jerry Endlich. President, Sterling Technology Group

Introduction

In our prior blog, we discussed the importance of putting together a longer-term analytics vision when considering a Business Intelligence (BI) investment. Of course there are many companies that have not yet started their full BI journey, and we hope they take to heart some of the main points from that article. On the other end of the spectrum, there are many companies that already have a mature, or semi-mature BI environment and are getting value from their data (and their investment). This is typically accomplished via meaningful reports and dashboards that enable better, more timely decision making in their organization.

And for next steps, many organizations, or at least the ones that want to be beat the competition are plotting followup steps in their analytics journey by integrating AI into their roadmap. The benefits can be enormous, and companies that are actively pursuing such a strategy are poised to be market leaders. According to IBM, data-driven companies are 178% more likely to outperform in revenue and profitability. And as with building your BI strategy, there are important points and considerations when crafting your AI strategy.

First, let’s understand a Data Fabric approach..

That next step around deploying critical AI components is predicated on understanding how an organization can further stretch the value of their data (in ways that were maybe not attainable 5 years ago). To do this, your data first needs to be seamlessly integrated from all sources (cloud or on-premise), easily accessible and easily understandable to even the novice user (in non-technical terms). The point is that data consumers, application developers, data stewards, business analysts, data scientists (etc, etc) should not have to constantly search for data, and not have to always request complex and time-consuming ETL processes from IT to curate data. Additionally, a best-practice approach is to also have the ability to consume your data from wherever it resides. No new Data Warehouses, no new Data Lakes and no complex ETL should be needed.

Basically, with a strong data fabric approach, anyone that needs data to enable better decision making should not have to care where it resides, or what format it is stored in. Additionally, all of it should be accessible from a single pane of glass, that is secure, easy to use, simple to understand and can be blended together. Plus, the data should integrate seamlessly with key platform components that will be part of your longer-term and larger AI vision (for instance to build and deploy AI/ML models).

So here is the elevator pitch and the value proposition: A Data Fabric provides one experience across hybrid cloud to ingest, explore, prepare, manage, govern and serve data for business-ready artificial intelligence (AI).

A word about Data Governance…

In order for users at any level of the enterprise to trust their data, an organization must first have processes in place to support the effective management and understanding of their data. That is where a Data Governance support system (or methodology) will help; and is the prerequisite for building out a bold AI and Data Fabric strategy. A meaningful set of governance policies (and maybe tools) will help your teams efficiently and securely locate and leverage trusted and meaningful data. (And, if needed, it may also help with any compliance related issues).

Once located, users can then confidently and comfortably share it while executing their AI initiatives. Cataloging data, developing ownership, being able to explain it and putting rules and business terms on the data are key first steps. Of course there are many platforms on the market that can help with these tasks. Making this happen is also a team sport. Meaning your organization at all levels needs to actively engage with subject matter experts, line of business leads and locate data evangelists/data stewards. Plus, executive support is a backbone of these initiatives and should not be overlooked.

Infusing AI, ML, Data Science.. and enabling better decision making.

Once your organization becomes a data champion, they should now be confident in moving forward with their AI strategy. And by the way, to start the journey it does not have to be all of your data, it can certainly be done in phases as long as there is an eye on the longer-term vision.

Moving forward means leveraging your great data in several ways. This should include infusing it throughout the organization, including: to build, deploy, monitor and scale AI/ML models, add predictive or further infuse vital information into your overall analytics and BI platforms. Of course there are other steps in the AI journey, and each step enhances the overall solution and ROI. Some of these steps (which will be discussed in followup articles) includes features that allow users to refine their own data, virtualize data, blend/join data (from different sources), automatically build/manage/evaluate AI models and then integrate these models and data into other enterprise applications (e.g. via APIs).

Moving forward..

Moving forward with a strong Data fabric will provide a complete set of consistent processes for efficient data integration and use throughout your organization (specifically when considering a move to AI). Additionally, many vendors are now also promoting integrated software to support this, although few take a holistic look at the challenges at hand. The main point is to come up with a plan, evaluate how your data can be further leveraged to provide meaningful information to key decision makers, consider reaching out to industry leaders that have a repeatable process and evaluate market tools that can assist in these processes. We think this will help your organization become a data-driven leader.

AI Briefing & Demo. Please contact us..

If your team would like to follow up on any of these topics (and also see our demo assets in this area), feel free to reach out to us. We would welcome an opportunity to discuss your use cases and strategic goals. You can contact me directly @ jendlich@gotoSterling.com, 914-649-2078

Also: click here to access our “AI Solution Briefing”, or here to setup a discussion and demo of our Data & AI assets.