When it comes to data structure, Google knows a thing or two. The tech giant basically structured the entire internet by encouraging website managers to implement certain SEO practices in order to achieve higher search rankings. Now, Google can organize millions of relevant search results in less than a second, enabling users to find exactly what they need with incredible efficiency using human language.
Like “googlers,” professionals at industrial companies should be able to quickly query internal databases and look up information about any physical asset. Analysts must be able to get what they need as soon as they need it, which is only possible if data is organized, labeled, and stored logically.
Those who can’t suffer from an “unstructured data” problem, the ninth problem in our data problem blog series. Unstructured data is burdensome to industrial organizations, especially those that manage many remote offices and track thousands of physical assets with smart sensors.
Taking the necessary data management steps upfront can alleviate many data-related headaches down the road.
Using Google to search the internet is easy because they take data structure seriously. | Photo by Benjamin Dada.
What causes unstructured data?
The unstructured data problem has several causes and is similar to the siloed data challenge in many ways.
Both can be caused by data that is stored and managed in regional field offices. Oftentimes, remote offices use whatever data management practices suit their unique needs best. Field workers have little incentive to change how data is structured, as they are only concerned with what happens within their territories.
Without data governance leadership or standards, industrial businesses are likely to have many inconsistencies in the way data is stored. At fast-growing companies with broad IoT ambitions, it’s especially easy to leave data management diligence behind.
Unstructured data also persists when manufacturers don’t have database experts in-house who know how to organize field intelligence for future use. Ultimately, IIoT deployments should generate data that is easily accessible, consistent, and meaningful. Without the right talent, it can be hard to maximize database design for all three purposes.
Who is affected by unstructured data?
Unstructured data primarily impacts data aggregators or those who need to find answers quickly from large sets of data.
These individuals have to reconcile differences across databases in various remote offices manually. Extracting, transforming, and loading data can quickly overwhelm analytics teams. If they don’t have consistent data structures, they spend mental resources keeping track of data hierarchies and schemas, which limits productivity. Industrial organizations that lack database expertise waste time and resources standardizing data, rather than processing it.
As with many industrial IoT data problems, issues related to unstructured data cascade up the leadership chain, making it harder for those at the top to make decisions. They have to engage with multiple databases and can’t refer to a single source of truth about their assets.
What do we need to consider?
There are two main factors to consider with data structure: governance and talent.
Transparent data governance and data management practices allow analysts to automate scripts that can accomplish complex tasks with tremendous efficiency. They can also train machine learning models that grow more sophisticated over time. With unstructured data caused by bad governance, these things aren’t possible.
Industrial companies must also hire database managers who can think high-level and far ahead when it comes to facilitating data access. Managers need to be able to implement the same hierarchies, relationships, and data structure everywhere. Organizations that hire people who can do so set their teams up for long-term success and increase the value of smart monitoring solutions.
Additionally, in the future, natural language queries will transform and democratize data access. Those without specialized coding or analytics skills will be able to query databases with simple voice requests. However, this will only be possible if industrial companies consistently structure data in a way that makes sense.
Like searching for a book in a library without a reference system, searching for data in an unstructured database could take employees too long. | Photo by Tobias Fischer
What’s at stake for your business?
Use the following questions to assess whether or not you have an unstructured data problem:
- Do you have a strong data governance policy with robust standards?
- Can your data analysts easily find any data point about any asset for any time period?
- Do you have a data structure in place for setting up new servers or databases?
- Do you have a single source of truth or knowledge base around your data structure?
By spending time upfront, industrial companies can mitigate many of the downstream problems related to unstructured data.
At WellAware, we help leaders streamline data management practices across their industrial IoT deployments. To learn more about our data structuring philosophy and approach, contact us today. Also, stay tuned for our last post in this series on the problem of insecure data!