Data infrastructure is becoming increasingly important to manufacturing and distribution companies, whether they are large or small. Yet, many of these companies may not realize what data infrastructure entails or recognize when they are experiencing business problems that could be solved by developing their data infrastructure to meet current and future needs.

So, what is data infrastructure? According to Techopedia, data infrastructure is the digital framework that is needed for promoting data consumption and sharing. You can think of it as being similar to the physical infrastructure in the world around us. It represents how data moves to the places it needs to go and to the people that need to use it.

Different companies might have different levels of data infrastructure depending on their needs around data, just like different communities might have different types of physical infrastructure. Smaller communities, for instance, have very rudimentary needs. Others that are larger need more complex infrastructure.

It’s the same at your company. If yours is a small company, you might only need a simple infrastructure for your data. Maybe the way people get the information they need is through word of mouth. If your company is growing quickly, though, you might have more complex needs to manage your data and make it accessible to those who need it.

Levels of Data Infrastructure

There are five basic levels of data infrastructure based on the size and needs of your organization. They are:

  1. Tribal Data Infrastructure. This is the organization that manages data by word of mouth. There are no formal processes around data, no systematic means of managing it. Information isn’t written down, so most questions in the organization are answered by asking someone who might know.The owner, or an organizational “elder,” likely has most of the tribal knowledge for the organization. And if that person doesn’t have the answer, then there may be no way to find out. This is the lowest level of data infrastructure; think of it as the digital version of a dirt road in the physical environment.
  2. Enforced Data Infrastructure. As companies grow beyond a few people and begin to incorporate people in multiple locations, word of mouth is no longer a sufficient means of managing the data needs that exist. At this level, the company is starting to systematize its data infrastructure with written procedures for commonly performed tasks, placing data into central repositories that may be accessible to more people and beginning to document workflows around data. This information is beginning to be kept in ways that provide access to teams rather than individuals. Think of this as a county-maintained blacktop road. Systems and processes are beginning to evolve, though they may involve manual processes, spreadsheets, or software applications that are used to complete specific business processes.
  3. Standardized Data Infrastructure. At this level, companies are beginning to look at improving their systems and processes, researching best practices in their industry and automating some of their more repetitive or menial data-related tasks. They’re taking the first steps to look across the enterprise—how to take data out of those process-oriented organizational “silos” and make it more accessible so it can help answer a wider range of business questions. They’re considering using broader solutions like an integrated ERP or data warehousing. A comparison in the physical environment would be a planned community that is building infrastructure like streets, sewers, power lines, etc. in anticipation of growth.
  4. Actualized Data Infrastructure. Organizations now have access to data across the enterprise and are beginning to discover what they can do with it. They’re beginning to find creative ways to use their data to solve business problems; they may even be looking at integration with supply chain partners to find ways to add value to the customer experience. In the physical environment, this would be similar to how a growing community needs to look at ways to attract business and keep traffic moving with highways, mass transit, and the development of business centers.
  5. Data Infrastructure Performance. At this stage, the organization has reached its goals regarding its abilities to collect, store, access, and manipulate data. It has the data it needs from a variety of sources and is able to use that data to solve important business and customer problems, similar to how cities are able to grow when all the physical infrastructure is in place to support expansion.

Why Is Data Infrastructure Important?

As indicated in the five levels of data infrastructure, data infrastructure is a necessary component for business growth in a similar way to how physical infrastructure is necessary for the growth of a community. When an organization only has a few employees, word of mouth can be a workable solution for managing very rudimentary forms of data, such as whether a particular customer has paid its bill.

But as the company grows, this solution becomes a barrier to growth. Claire in accounting can end up on the phone all day answering questions instead of doing her actual job. The organization needs more definitive and easily accessible answers on more and more topics, which means it needs more efficient ways to gather, use, and disseminate data. This is what data infrastructure allows you to do.

What are the Elements of Data Infrastructure?

Data infrastructure can be roughly divided into four data infrastructure elements that will give the company access to the data it needs to solve customer and business problems. If you want to develop an infrastructure around your data, here are the elements you’ll need to consider.

  1. Data Collection/Big Data. How is your organization going to access the data? Where is it going to come from? Organizations can collect data internally within the company, as well as from outside sources. In fact, as we enter the age of big data, the number of sources that a company can use to get answers to its business questions is nearly endless. You can mine for information from your own internal sources to getsales order management data, financials and warehouse data, shipping data, and much more. You can also look at outside sources such as social media to get answers about customer wants and needs. As technology like the Internet of Things develops, you’ll even be able to look downstream beyond the point of sale to gather data about how products are actually used in the environment; this will provide comprehensive data to answer tough questions like, “How do we improve quality and customer satisfaction?”
  2. Data Storage. One of the biggest challenges around data infrastructure in the age of big data is, once you’ve got all this data, where do you keep it? Data storage requires space. This can take the form of space on your server, or it can be located on a cloud-based data storage solution, a data lake, or a data warehouse. There are obvious limitations around using the in-house server model to store data; for many smaller companies, the cost to purchase the physical space needed would be exorbitant. The cloud-based approach is a good fit for many businesses due to its flexibility and ability to scale according to the needs of your company. Using data lakes or warehouses can also be a good fit for companies with larger data needs.
  3. Data Analysis. Once you’ve got the right data and a place to put it, what do you do with it? This is where data analysis comes into play. Once you’ve formulated the questions you want your data to answer, you’ll need to identify the data that can answer those questions, cleanse the data, and format it so that you can use it in your data analysis tool. You’ll build an analytic model and plug your data in to draw a conclusion.
  4. Data Reporting. Of course, your analysis has to be interpreted for the audiences that will be making decisions in order to provide actionable insights. Nobody is going to wade through 100 pages of data and analysis to try to figure out what the answer to their question is supposed to be. So, the data needs to be “pre-digested,” or made accessible, to the audiences that need to use it. So good reporting, often in the form of data visualization, may be necessary to help decision makers “make sense” of your results. A number of reporting and visualization tools exist that can be useful for this purpose, from simple tools for straightforward problems to more complex solutions that might meet the needs of a global enterprise.

Creating systems and processes that manage data is crucial as a company grows. A strong data infrastructure serves as the foundation for answering key business questions. What is your organization’s approach towards data infrastructure? We’d love to hear about it in the comments.