3 Ways Data-Driven Organizations Enable the Future of Work

Two disciplines, becoming a “data-driven organization” and enabling the “future of work” are related objectives, and excelling at one can accelerate the other.

We seek data-driven organizations where leaders use data and rely less on intuition when making decisions. We want to help all employees access data, analytics, and the tools to ask questions, discover data sources, and expose potential answers with data visualizations.

Essential digital transformation practices by Isaac Sacolick

All organizations must find ways to use data, analytics, and machine learning to strategic and competitive advantages. A report from DataStax shows that an organization-wide strategic focus on real-time data increases the likelihood of a “transformative impact” on revenue growth by 2.3 times.

The future of work means many things to different people. Improving the culture. Excelling at diversity, equity, and inclusion. Driving sustainability. Automating more repetitive tasks and accelerating innovation practices. Supporting hybrid working, collaboration, and globally dispersed teams. McKinsey shared 56 foundational skills in four categories: Cognitive, interpersonal, self-leadership, and digital, while MIT reports people in data-driven organizations are data literate and comfortable working with AI.

Enable the future of work with these five data-driven practices

In my new book, Digital Trailblazer, I share several of my stories about leading data-driven organizations, from marketing departments adopting citizen data science to nonprofit organizations investing in dataops.

So how does enabling a data-driven organization prepare them for the future of work? Here are three ways

Automate dataops, but encourage data prep

Many organizations still have departments and outsourced teams handling data operations with tasks to load data sets in manually. They run scripts, check for errors, and make manual data quality fixes.

Other organizations have automated some dataops, but the process and skills are siloed to a department, selected data sets, or embedded in one application.   

And while many orgs have data prep tools, they are more often used by a select few analysts when exploring new data sets. There’s often a disconnect in operationalizing their work and taking their prep into a production process.

Creating standards and centralizing some dataops activities is a key responsibility of chief data officers, yet getting the organization onboard with their charters isn’t easy. Attack this challenge because it’s hard to be data-driven and seek data literacy when the data pipelines are a tangled mess of technical and data debt.

Centralize data catalogs, data dictionaries, and ML training data

When a waterfall fills a pristine lake three miles into the forest, is there a well-marked trail for people to find it? Once I’m at the lake, are there clearly identified steps to easily find the waterfall and climb it to its source safely?

You have lots of data sources, and maybe you’ve centralized them in a data lake, but that doesn’t mean employees know how to find usable data sets, have access to them, or understand the policies on how they can and cannot use them. That’s the role of data catalogs and why they are key platforms to enable transformation.

But data-driven organizations do more than cataloging their data sources.

Survey your CRM and count how many date, currency, category, and attribute fields people must understand before using them in their analysis. Proactive data governance practices, including creating data dictionaries and profiling data sets, help people understand data’s meaning before they apply it to their analysis and decision-making.

Lastly, organizations with many data scientists working independently in different departments recognize the cost and complexity of creating ML training data. They’ll take steps to centralize this data and make it easier for more people to link and load it into their own machine learning and analytical models.

Ban presenting data in PowerPoint, Spreadsheets, and Tools Disconnected from Data Sources

Data driven organization by StarCIO and Isaac Sacolick

In my first book, Driving Digital, I share one of my data center of excellence frameworks for getting executives on board with data-driven practices. It requires taking the PowerPoints and spreadsheets away and presenting real-time data directly from workflow tools (CRMs, ERPs) or analytics solutions (Tableau, PowerBI) at meetings. I tell the stories behind the framework in Digital Trailblazer.  

The “past of work” had only a few people that could wrangle their way to meaningful data analysis, presentation, and storytelling. Today, these responsibilities must be democratized, but a Digital Trailblazer’s efforts will fall short if the executives in the room still want their insights presented as 5-course meals with creative plating that masks what goes on behind the scenes to prep and cleanse ingredients.

Several additional practices of evolving data-driven cultures help enable the future of work, and I’ll share two more ways in an upcoming episode of the Driving Digital Standup. Sign up for the monthly Driving Digital Newsletter and get an alert when it’s up. 

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