How do organizations work with data when it's distributed, heterogeneous and vast?
Data enables every organization to obtain visibility and achieve transformation in operations and business model. However, data is distributed, heterogeneous, and vast. How do organizations effectively and efficiently work with the rapid influx of this new asset?
Every organization is collecting data today. Whether it is from sensors, or physical equipment, or customer transactions, or user behaviors, data is affecting and changing our lives. However, working with data is far from easy. With the rapid influx of raw data coming into what’s called data lakes, the lakes are overflowing. Managing data becomes an imperative task for every organization. This article talks about several ways organizations can work with data more effectively and efficiently.
1. Turn raw data into structured data as early as possible
Organizations should format raw data as soon as they are generated. Data conversion and formatting can become a complex and time-consuming process once it enters the data lake. At the same time, meaningful metadata that provides context to the data acquired should be added and preserved. This makes it easier for the transformation of raw data into human-readable formats. This process is also known as “ETL”, or extract, transform and load. This prepares data for to be better utilized for processing, analytics, and obtaining valuable understanding of it.
2. Reduce the data set as much as possible
Most data we collect is not useful. What kind of meaningful results can we extract from raw data? Can raw data be concretized and converted into conditions or events? Reducing the data set earlier on not only helps remove distractions and reduce the workload during data analysis, but also helps reveal the possible ways the raw data set can be used by pushing us to question why we choose to keep or remove certain parts of a data set.
3. Extract insights out of data as soon as possible
The earlier you work with the data, the more value you can get out of it. In fact, working on the data in real-time as it is being generated can provide a sense of urgency to understand the value of the data. Once you’ve derived all the insights you can, throw the data into a data lake or AI engine.
What does this all mean?
All the above points amount to one rule: work with data as early as possible. Working with data earlier on makes it easier for you to understand where it came from, what properties they have, and how you can use them. Even if AI engines can help crunch large amounts of data, in most cases, it should be used as a last resort after you exhausted physics-based models and analyses.
Keep your data frameworks flexible
Data is highly heterogenous and customized, and it will change over time. Your framework must be able to handle not only various data formats but also changes in the data structure. Adhere to open standards and open ecosystems – any proprietary data structure will eventually become obsolete.
Data transformation happens in phases
Don’t expect data transformation to happen overnight! In our article, “Overcoming challenges during IoT adoption,” we explained that IoT adoption happens in 3 phases: visibility, discovery, and transformation. The same phases apply to data transformation. Accept that some experiments will fail, but take small and quick steps, and you’ll discover the optimal way to take full advantage of the data you have.