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May 5, 2020
Data Intelligence

Four Tangible Ways to Kick-Start Your Data Strategy in 2020

Data is the new currency. 

According to Gartner, there are 30 new enterprise IoT devices connecting in the world each and every second. And, by the end of 2020, it’s estimated that over five billion enterprise IoT devices will be in service worldwide.

All of that data creates real anxiety (in fact, we recently wrote about coping with data anxiety in an IoT world), especially for those leaders who are responsible for the collection, analysis, governance, and security of massive amounts of data.

Those organizations who are able to harness data effectively will reap the benefits much more quickly than their non-data-driven counterparts. According to Forrester, data-driven organizations grow at an average of more than 30% annually and are on track to earn $1.8 trillion by 2022.

But all that data—without an effective strategy—means nothing. You must learn to control and leverage the right data in order to unlock the full potential of your team and organization.

Now, more than ever, you need to make sure that you’ve implemented a data practice that’s built to capture, cleanse, analyze, and present all of this data in a way that allows your business to make smarter, more outcome-focused decisions.

Here are four ways you can kick-start your enterprise data strategy in 2020:

1. Align Your Strategy with Key Objectives

Make sure that your data initiative’s outcomes ladder-up to business objectives; your data and insights are useless if they don’t provide the business any value.

According to Forbes, nearly 50 percent of businesses say big data and analytics have fundamentally changed business practices in their sales and marketing departments; nearly 30 percent have said so in research and development. 

Your business strategy and data strategy should be complimentary; they should work together in a symbiotic relationship. This arrangement ensures that your data strategy has a firm, operational foundation. 

Your goal is to become an organization that uses insights gleaned from data to push your business forward. This is where innovation comes into play: Collecting and analyzing data will expose inefficiencies and allow you to hone-in on specific areas of your organization.

2. Ensure Sound Data Governance

Your organization creates massive amounts of data each and every day; those data sets are only as useful as the governance measures put in place to manage that data effectively.

That’s why, in a data-driven world, governance—the way you manage the people, process, policies, and culture around data—should be at the top of any organization’s priority list. This means ensuring accessibility, usability, privacy, security, and other factors that generate value for the business. 

A lack of proper data governance measures can can have stifling effects on your business, many times resulting in misinformed decisions. That's why it's of paramount importance that you're continuously working to standardize, implement, and re-evaluate data governance measures.

Teams and organizations who implement sound data governance measures experience better, more comprehensive decision-making capabilities, reduced costs in terms of data management, and increased efficiency, costs, and compliance.

If you're not sure where to start or are still fairly early in your data journey, here are several, important questions that you should be asking in order to ensure sound data governance:

  • Who is in charge of collecting and storing the data?
  • Who is keeping all of this data up-to-date? 
  • Is all data accessible by the necessary parties?
  • What permissions are needed to access the data?
  • Have you established and/or updated a Data Code of Ethics?
  • How can you ensure that use of the data is open and honest across the organization?

3. Maintain Quality Control

The sheer volume of data that your business generates each day requires intentional methods to systematically manage and control data assets; effective quality control is mandatory.

While the governance methods you put in place help to increase the likelihood that your data systems are operating efficiently, it's obvious (yet critical) to make sure that the actual data you are sourcing is clean, accurate, and actionable. 

Outdated, inaccurate, or unreliable data has massive, cascading effects that trickle out to every corner of your business. Implementing strong data quality management practices builds a firm foundation ensures that the bedrock of your data strategy is on target.

Therefore, you must keep a trained eye on data quality. Doing so might mean evaluating your data sets against multiple dimensions: including accuracy of key attributes, completeness of all required attributes, consistency across multiple data sets, and timeliness. Thankfully, there are tools and technologies (AI and ML, for instance) that can help you examine the base rules for data equality, pick up on errors or inconsistencies within the data, and then suggest improvements as the real-world changes in data become visible as exceptions or outliers.

If you’re just getting started, or you’re re-examining your data strategy entirely, start by looking at some of the basics: for instance, are there organizational standards in place that govern the labeling and storage of critical data? Are there methods of normalizing the data so that the right people, system, and tools can easily access the data? Answering these questions (and others like them) should help you identify common data uses, patterns, and queries.

4. Develop Better Data Presentation Methods

The list of people and systems that now need to be able to view, interpret, and analyze data points continues to grow exponentially. So, too, should your data presentation capabilities.

Each of these actors must also be able to independently make mission-critical decisions based on these data sets, which means that most organizations —in addition to optimizing their data structure—are likely due for a significant upgrade in the area of data visualization.

It’s more important than ever that teams have the processes, standards, and tooling in place to quickly convert raw data into easily understandable, actionable formats for various audiences.

Thus, it's your job to make sure that you're making time for regular, outcome-focused conversations with key stakeholders within the organization. Doing so will better help you understand the needs of both individuals and individual business units.

Almost assuredly, these types of every-day conversations will help you uncover areas where: (1) your existing data presentation methods aren't effective, and/or (2) there's legitimate opportunity for you or your team to bring a more mature set of data presentation methods or tools to the table.

Wrapping Up

Data is arguably your organization’s most valuable asset, if structured and utilized properly. The importance of a sound data strategy cannot be overstated; it allows you, your team, and your organization to make decisions with more confidence, mitigate risk, identify inefficiencies, and gain a better understanding of how your organization operates. 

Want to read more about the steps you should be taking in order to become a more data-driven organization? You can read more from our team in The Enterprise Leader's Guide to Creating a More Adaptable Organization.

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RevUnit is a technology studio that helps supply chain clients identify and implement data solutions that actually prove ROI. We help organizations across industries like transportation, freight, logistics, retail, and manufacturing achieve business results through innovative data solutions — powered by AI/ML and the cloud. We’ve done it for clients like ArcBest, J.B. Hunt, and Walmart.

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