In a modern business of any size, data is what makes or breaks you. It’s what gives your company a competitive edge—or can fail you if not used properly. It’s the raw material of an informed, dynamic corporation—as essential to revenue as iron ore is to a steel manufacturer or wood is for a paper mill.
Today’s challenge is not so much about gathering data as a resource (if anything, there’s too much data to go around) but rather about employing it effectively and safely. In this first of a series of posts, I’ll look closely at the various sources of data that flow into an enterprise and begin to understand the need for enterprise data technology (EDT)—the collection of tools and processes that help a company harness the power of its data. In later posts, I’ll look at the specific systems and applications of EDT, then explore current EDT trends, including AI.
By way of background, much of my three-decade career has focused on analyzing, selling or marketing enterprise data and the solutions that manage it. I started with a software company that developed an enterprise resource planning (ERP) solution for the chemical manufacturing and distribution industries. In that capacity, I had to show prospects and customers how essential data was for managing sales orders, monitoring inventory, processing work orders and planning overall operational capacity.
Data was essential then, and it’s even more essential now. Digital transformation cannot happen without a holistic understanding of data and its value. That applies to everyone and every function within a modern corporation, to one degree or another.
The confounding array of enterprise data sources
To clarify, these articles focus on the types of information typically associated with large corporations. The data management issues of a 10-person firm are noteworthy but on a different scale than a corporation with thousands of employees spread across the country or around the globe. Coordinating data across such an expansive organization can’t be handled by people alone, as becomes evident once you dig into the details. So, let’s investigate.
Enterprise data includes structured data, such as records in spreadsheets and relational databases, and unstructured data, such as freeform text, images and video content. Structured data is, by definition, recorded in a way that facilitates its retrieval. Unstructured data? Not so much. That’s our first hint about how difficult it can be to manage enterprise data effectively.
We also further differentiate between data that is internal and external. As we dig into the many varieties of each of these categories below, you’ll be reminded why this distinction can be important and just how complex it can be to manage information from so many disparate sources.
Internal data: The information used for running the enterprise
Internal data is exactly what it sounds like: all the internally produced data created in the course of operating a company. It includes the following:
- Operational data — Including transaction data about sales and purchases; inventory data regarding raw materials, finished goods and inventory levels; and financial data regarding revenues, costs and profits
- Human resources data — Employee information including profiles, performance, payroll, attendance and training records
- Infrastructure data — Details on the company’s physical assets, properties and IT infrastructure
- Communication data — Records of internal emails, notes, memos and minutes of meetings
- Research and development data — Information from research projects, product development stages and testing results
- Customer data — Contact details, demographic information, purchase history, loyalty program data, customer service feedback and other interactions
Got all that? It’s a lot, but we still haven’t looked at the data that comes from outside the walls.
External data: Making sense of what’s happening beyond the enterprise
Now we shift to looking at all the external data sources that companies pull in so they can operate more intelligently. These include:
- Market data — Including competitor analysis; data on product offerings, pricing and marketing campaigns; and trend analysis of the market, emerging sectors and negative issues
- Customer data — Including social media postings, user reviews and other data generated by customer inputs
- Economic indicators — Related to the economy, both domestic and globally
- Regulatory and compliance data — Relevant to industry regulations, standards and compliance requirements
- Environmental data — Concerning environmental conditions pertinent to certain industries and activities
- Third-party data — For example from analysts or market research firms
- Social and news media feeds — From news channels, blogs, forums and social media platforms
- Demographic and geographic data — Information on populations, age groups, cultural nuances and geographic distribution, which is essential for market segmentation and targeting
Why all of this matters: opportunities and pitfalls
As you begin to think about all this information, you can see both the positives and negatives that it entails. The positives are that you can find many ways of growing revenue through new products and services by accumulating, processing and analyzing these data sources effectively. You also can find techniques to curb waste and run more efficiently.
But this only happens if the enterprise integrates these data sources so that these possibilities are not overlooked. For instance, as a product team looks to develop new offerings, it needs to have access to external demographic and geographic data, as well as internal research data. Meanwhile, business intelligence and supply chain management staff would need to monitor inventory levels and relevant consumer buying patterns to make timely and informed purchases. The marketing department needs to understand market demographics and monitor customer sentiments. And so on.
The negatives are clear, too. With so many sources of data, how do you corral it all? Unless you take a thoughtful approach, you could be flooded with unorganized data that’s hard to collate, hard to analyze and hard to keep secure.
Large, leading-edge enterprises employ enterprise data technology to master their data sources and gain advantages over their competitors. For example, Amazon and Netflix analyze customer behavior to make personalized recommendations. Uber uses EDT to analyze driver and rider data to optimize routes, pricing and customer experiences. Oracle, Microsoft and Salesforce sell CRM software that provides critical data to businesses so they can achieve operational, marketing and revenue objectives. And these examples could go on for another ten pages.
In my next installment, I’ll explore the tools, platforms and methodologies that constitute enterprise data technology and why professionals outside of IT should make a point of understanding the functionality of EDT in a modern corporation.