As the practice of collecting, processing, and storing data becomes more complex, the value and quality of that information can be at greater risk. Those insights and trends leaders have come to rely on may no longer be accurate. And that can lead to the types of decisions and conclusions big data is supposed to help companies avoid.
With increased complexity comes increased potential points for failure, and businesses are finding the need to simplify. At the very least, there’s a need to find ways to manage big data’s complexity in order to ensure data quality. From consolidating data collection methods and merging records to using technologies like data observability applications, various techniques can help.
Complexity happens when technologies start to overlap. Companies continue to bring in new ways of collecting and analyzing data without evaluating how to better manage the new complexity. Getting back to the basics is a lot like decluttering—think Marie Kondo. However, if throwing out all your data systems is not an option, here are some tips to get more clarity and efficiency from your existing, complicated systems.
Purge Your Data
Think about all the different information your business collects—structured customer data, unstructured data such as video data, IoT information, email and text data, and more. No matter where it originates, ultimately all the data has to be cleaned and de-duplicated so that critical business decisions can be made.
Do you have various systems collecting and storing the same data? Or systems that essentially perform the same function? It’s not uncommon for this to happen as organizations grow and/or merge.
For example, a separate equipment assets database that employees have to manually manipulate may no longer make sense. That’s especially true if the company is also using an updated application that’s been capturing the same information via automation.
Purging your data begins with prioritizing what information you need to collect and determining whether there’s unnecessary duplication. Consolidating systems and platforms can reduce repetition and unnecessary tasks. Consolidation can also increase clarity about what the data points to and how it fits into the overall picture.
If multiple systems are collecting identical information, there may be many records about the same thing. It’s even likely that the format of those records doesn’t match exactly, creating an additional layer of confusion and complexity.
For example, are manually entered product codes in different formats than those captured by barcode scanners? An important part of the merging process is figuring out what’s reliable and matching records about the same data. Although 100% accuracy isn’t always realistic, scattered records that look like different entities on the surface create false impressions.
Scrubbing your records can take some time and manual power. You’ll probably need to determine which formats are acceptable and whether there are any inaccuracies. Customer addresses, for instance, can be in forms that don’t match official U.S. postal records. There may be some addresses with missing ZIP codes or street abbreviations that aren’t uniform.
You may also need to establish new processes for data quality audits. Scrubbing and merging your records is often an ongoing task that changes as new technologies and applications start to overlap.
Use AI and Automated Tech
Human-driven processes and today’s sophisticated technologies, like the internet of things, are what lead to data complexity. The good news is that software that helps sort through these variances and why they happen can identify issues you can’t address manually.
Applications that observe, track, and troubleshoot how data flows throughout the organization can reduce complexity in a few ways. Observability apps can help explain why processes and workflows fail. These platforms can also signal when source systems change and new patterns in data emerge so that you can see the impact of these changes across your organization. Overall, these applications make sure that your company’s data is clean, timely and accurate.
Identifying critical information can involve looking at the age of the data, how recently it was consumed by your users and whether or not it makes sense to keep the data. Observability apps can give you a sense of the age of your data. Is your data hot, warm, or cold? There may be information in one database or platform that was entered some time ago and was never modified. Observability apps help you discover what data to delete or purge.
Observability applications can expose how changes in source systems can affect data in your company. Is there something missing in the data that’s creating inefficiencies? Or are there departments making changes to the information that affect other departments or downstream reports? Is this causing inconsistencies and misinterpretations by other teams that create bottlenecks or points of failure?
Observability can indicate how data flows through separate platforms and whether changes in one application produce inaccuracies in another. Reducing inaccuracies and disjointed information may mean finding a way to get those applications to connect or rely on the same database. An example could be separate CRM and billing platforms that both collect contact details for customers. Observability apps could expose contact information that is not syncing, causing inconsistencies and errors in customer communication.
Common Approaches to an Individual Process
Reducing the complexity of data analytics can be a distinctive process for each organization. It’s dependent on how many technologies and processes are in use as well as individual cultures.
However, automation and AI provide insights and common approaches that can reshape and simplify the data a company relies on for making strategic decisions. Through this insight comes simplification of complexity and increased quality and accuracy that can remove many of the hiccups associated with data-driven initiatives.