How to become a “data company”

In almost every industry, data companies are dominating.

What is a data company?

A data company is any business that has made the collection and application of data one of its core competencies.

Data companies can be tech behemoths like Facebook, Google, or Amazon. Non-tech companies like Walmart or Target are also getting in on the action.

With the rise of increasingly affordable business intelligence (BI) tools, even small or medium local businesses are becoming disruptive data companies.

Size and industry doesn’t matter. All that counts is how you use your data.

Virtually any business can become a data company by following these simple steps:

Step 1: Have a Data Strategy
Step 2: Centralize and Model Your Data
Step 3: Visualize Your Data (Reporting and Analytics)
Step 4: Use Advanced Analytics


Step 1: Have a Data Strategy

Every great data company has a data strategy–a clear vision of the role data plays in your business and what you need to do to make sure your data is always full leveraged.

To create your own data strategy, start by listing all your all data sources. Inventory data, customer reviews, website traffic, email lists, and financial reports are just a few examples.

Next identify opportunities in your data to make your business more profitable. A great way to do this is to think of questions that if answered, could help you either increase revenue or reduce costs.

For instance, you may want to know which of your products and services have the highest ROI, or which prospecting activities are most likely to lead to a sale.

Good questions always tie back to your bottom line and need to be answered on a regular basis.

Once you’ve identified a handful of high-value questions, identify which of your sources you’ll need to get answers.


Step 2: Centralize Your Data

Step 2 is the step that most businesses and skip, and consequently, it’s also the number one reason most businesses that set out to become data companies fail.

You may have noticed in Step 1 that many of your highest-value questions require data from multiple sources. This can be a serious challenge, especially if two of your data sources aren’t inherently compatible.

If you’re not careful, you could end up bogged down manually collecting and combining your data in Excel spreadsheets. Doing things this way is tedious, error-prone, and unsustainable.

Spreadsheets on their own can work for smaller businesses that aren’t growing. For anyone else, spreadsheets eventually need to be supplemented with more robust reporting tools.

The best way to collect and centralize your data is with a data warehouse.

A data warehouse isn’t just for storage. It’s a special database where your data is stitched together to make “one version of the truth” for your company.

Data warehouses also let you define all your business logic in a single location. This keeps your data error-free and helps you avoid getting conflicting numbers.

Many businesses try to skip Step 2 and go straight to Step 3: visualizing their data with reporting tools like Power BI, Tableau, or Domo. 99% of the time, this is a huge mistake.

While “viz tools” are great for building dashboards, graphs, and reports, they tend to overpromise on their ability to connect to your data sources. At the end of the day, viz tools simply work best when they’re used in conjunction with a data warehouse.

Building and maintaining a data warehouse is no easy task. It requires buying expensive hardware and software, and hiring a team of skilled BI developers.

If these challenges sound daunting, consider using a Done-For-You Data Warehouse instead. It’s a great alternative that’s cheaper and more effective than building your own data warehouse! Best of all, it’s a totally hassle-free data warehouse solution!


Step 3: Visualize Your Data (Reporting and Analytics)

With your data properly centralized and modeled in your data warehouse, you’re ready to visualize it.

Data visualization is the process of turning your data into meaningful charts, graphs, and other visual representations that can be used to help you make smarter decisions.

Well-made dashboards and reports can be easily shared with anyone in your organization.

There is no shortage of great viz tools for completing this step. Some of the top options are Power BI, Tableau, Domo, Grow, Qlik, and Looker. You could even keep using Excel!

With a data warehouse built underneath to support it, almost any viz tool you choose will do the job. Plus, with your business logic and data already in one place, it’s easy to try out different tools until you find one that feels right for your business.

Now you’re finally ready to start getting answers to the questions you asked in Step 1.

As you get more comfortable with your new business intelligence system, keep identifying and answering additional questions.


Step 4: Advanced Analytics

Visualizing your data in reports is great, but it’s just the tip of the iceberg in terms of what your new data warehouse can help you accomplish. To really take advantage of your new BI setup, you need to start conducting advanced analytics.

Basic analytics are about reporting where you’re at or where you’ve been (how much inventory do we have, what were our total sales last month, etc.). Advanced analytics help you see where you’re going (how much will sales increase if we open a new location, what are our projected earnings based on past performance, what is the average lifetime value of our customers…)

Applications of advanced analytics include what-if analysis for exploring different strategic paths, and even more sophisticated capabilities, like machine learning.

The advanced analytics process uses powerful mathematical techniques to interpret data. Patterns, groupings, and correlations in data sets are identified using statistical methods and machine-based techniques, such as deep learning. This allows you to make predictions about future behavior.

These complex predictive and prescriptive analyses often require a highly skilled data scientist who knows computer coding languages, like Python and the R language.

Again, you can hire your own, or, just use a Done-For-You Data Warehouse services and have your data science work taken care of for you by seasoned data experts.

With the advent of big data, advanced analytics is becoming more and more common. Either you get with the times and start taking advantage of your data’s potential, or you get left behind.


Next Steps

With you data strategy, data warehouse, viz tool, and advanced analytics, you can use your data to quickly and efficiently launch new BI initiatives and grow your business.

Test new products and services, measure and optimize your marketing efforts, or help your employees be more productive with KPIs. The sky is the limit!

Visit our knowledge center for more ideas on what to do with your data.

Nate McMurtrey loves using data to solve business problems. He is a Business Intelligence expert, data warehouse wizard, and pioneer in Done-For-You data warehouse solutions. He has worked as a BI manager and freelance BI consultant. In 2014, he co-founded Xerva with Nate Allphin.