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In these uncertain times, effective understanding of your customers, your employees, and your market will illuminate paths for managing the short-term so as to be able to continue to grow and even flourish after the COVID-19 outbreak, a period that most economists think will be measured in months and not years. Companies have critical questions to answer about the shape and size of their workforces, including decisions on whether to furlough workers, reduce workforce size, or re-allocate teams to focus on high priorities. Some companies will even choose to grow and invest now, trading a short-term profit hit for long-term market share.
As the world continues to navigate the pandemic’s coming months, the time is now to rethink your company’s analytics strategy. One of the cornerstones of any Data Strategy is data availability, and many companies have been turning to data catalogs to help inventory that data. The smartest of these companies are able to use this catalog as a self-service data marketplace to rapidly get the right data to the right people to support an increasingly data literate workforce.
We have a model for how this should work in our personal lives. Every day, we experience the simplicity and convenience of shopping online — a global virtual marketplace that has increasingly replaced the physical one, even more so during our current pandemic. We shop for everything online — groceries, clothing, and entertainment (streaming video content). When we shop online or go to iTunes/Amazon Play, based on our data-driven profile and interests, the shopping experience helps us find what we want and saves us from looking at items that wouldn’t be of interest. It’s simple, and it saves time and money too. That experience is seamless due to a very sophisticated integration of a catalog for shopping, a robust supply chain, and a careful approach to data that can translate SKUs and GPS coordinates into information that we can use to see where our products are in the pipeline.
The time has come to make accessing data as simple as downloading new music. And smart companies are embracing this kind of simple self-service by embracing a few key principles.
Create a Simple Way for the Business to Find and Use Data
IDC forecasts a ten-fold increase in worldwide data by 2025. Data is everywhere, but we struggle to find what we need, make sense of it, and turn data into insights and action.
According to a Deloitte 2020 Retail Industry Outlook, convenience matters — now more than ever. The retail industry has invested mightily in technology to understand a consumer’s wants, suggesting products that are on point based on data-driven insights. When you shop online, the processes are integrated — from initial search intent, pricing, supply management, and purchase — with data flowing through these steps cleanly and transparently. Through a data marketplace, supported by a data catalog, any business can enable employees to search for relevant data and supply it directly from any source (cloud, on-premise) to their analytics software instance for insight creation.
Implement Data Governance that Creates Confidence
As you shop online in your personal life, you don’t always have the knowledge to pick a specific product by brand or feature. You might leverage a search to narrow down candidates, and then you might read reviews to gauge people’s satisfaction with the product. You might also conclude that what you really need is a replacement part. And you might conclude that the product that you desire is not available in your location based on local laws or shipment costs. These are all excellent examples of product metadata that drive the shopping experience, each of which requires dedicated resourcing to address.
A data catalog must similarly allow you to find data that you might not have known about. It should allow you to see relative quality through labels, profile assessments, and comments. And finally, it should restrict access to data that is not appropriate for an individual based on role or use case. The data catalog enables the data marketplace approach at scale by automating the ability to onboard, profile, describe, secure, and potentially prepare data quickly in anticipation of analytics needs but requires formal governance to make sure that what appears on the shelves is understood.
Stock Data Before You Need It
When you shop for goods, the products that you desire are generally built and in a warehouse waiting for your order. A thoughtful supply chain has created a demand plan well in advance of the purchase that anticipated this very moment. If everything that we ordered was a make-to-order experience, items would take weeks to arrive. Data should be no different. Processes should aim to put data on the shelves in advance of the analytics execution, which requires speed measured in hours rather than weeks. Many organizations start a search for data when the first request is made. This is already too late. Data Governance, in this context, should create a data demand model that anticipates data requirements based on organizational priorities in order to build up a meaningful baseline of useful data that can grow over time.
Drive Data Literacy
The concept of self-service relies on the fact that the person accessing data has the ability to use that data. The foundation of this is Data Literacy. Smart companies are investing in boosting the ability of their teams to read, work with, analyze, and argue with data and elevating skills across the organization, from descriptive analytics to the use of complex predictive machine learning models. If we follow our metaphor, a home improvement store could never sell 2x4s if there were no construction skills. Similarly, data is worthless without the ability to perform meaningful analysis and draw meaningful conclusions.
Shorten Time from Data to Action Through DataOps
Data is a team sport. The governance function makes the data available. Business analysts and data scientists create insights. But roles that should not go overlooked are the data engineers and application developers, who are responsible for deploying analytics solutions to drive value. The successful data catalog should make the ability to integrate with both the analytics tooling landscape, as well as the DataOps pipeline, effortless. If efforts are made to include these roles in development teams and to integrate the data catalog with analytics tools and deployment technology, the time to value moves to hyperspeed.
DataOps is the emerging practice that takes a DevOps approach to data by addressing the people, process, and technology challenges to creating a data culture. The organization needs to ask a number of questions: Is the analytic performant in real-time? Is it clearly embedded in an application in a seamless way? Is the analytics having the desired impact? Should we change our approach? Is the challenge one of concept, analytic utility, or deployment option? This closed-loop process will provide a critical link back to the non-technical community and allow for clear measurement.
There has never been a better time to put a strategy into practice to empower your organization with data. Similar to online retail, winners in all markets will put significant emphasis on understanding one of the company’s biggest assets — data — and how they empower their other biggest asset, their people, to work with and use data to glean insights and drive outcomes. Driving a culture of Data Literacy within a DataOps context with your data marketplace at the center is a clear recipe for success.