Big data is one of the most important aspects of digital transformation. What exactly does the term mean and how does it fit into an organization?
The concept of “big data” has been talked about for years, and it's going to continue to be an essential strategy for both digital transformations and organizations overall. With a proliferation of new technologies manifests a lot of data. How can companies leverage these powerful insights to create actionable strategies? Let’s dig in.
Technology influences many parts of everyday life, not just for organizations but for consumers and individuals as well. With fast technology comes high-speed data. Increased number of technologies constitutes larger data captures. Organizations that are deploying new systems are often oftentimes don't know what to do with this volume of data.
It’s not just internal data like financial information, work in progress, and tracking the inventory in a warehouse. There are also external data too. Big data comes from suppliers, customers, and all different parts of the overall supply chain. The key is to excavate findings and actions for data, both internal and external.
In addition to internal and external data, there is also structured and unstructured data. Structured data is more tangible and measurable, something like a financial transaction. There are certain dollar amounts or monetary value that are placed on a transaction. There is also unstructured data, things like social media engagement, including comments and emails received from customers. Unstructured data isn't necessarily quantitative, but it's data that can be a great asset if the right toolsets are used.
For organizations that are going through digital transformation, data can be very decentralized and reside in spreadsheets. This is a challenge with big data that often gets overlooked in terms of the volumes of data. This information may not be easily accessible by the rest of the organization.
One of the keys to understanding big data is to evaluate the origins of big data and the location of data points. This overall awareness will help identify strategies to best leverage this high volume of insights.
Data shouldn't an accident. It should be an intentional initiative that's strategic. For example, gaining a competitive advantage. Organizations understand their data footprint and can accurately capture and centralize this information have a competitive advantage versus others that do not. One of the ways to leverage that competitive advantage is to integrate tools like business intelligence, predictive analytics, artificial intelligence, and machine learning.
These are four different tools that activate and create value for all the data being collected. Artificial intelligence is a great example of working to predict future business trends and consumer behavior. The best way to effectively leverage this strategy is to have efficient, clean data internally and externally.
The healthier the data is, the more effective the business is going to be in creating a competitive advantage over the rest of the field.
With these massive amounts of data and analytical tools, comes the need for better and faster processing. This is where just general computing power must keep up. There are certain types of technologies that are better at processing and transacting data than others.
For example, there are software vendors like SAP which they have created the S/4HANA platform to assist organizations. This is essentially a database that's designed to create faster, real-time data performance and visibility. In the past, there used to be batches of data that would run overnight. These batches would not be ready until the next morning. SAP's S/4HANA, provides real-time visibility right as transactions are happening, which quickly produces the processing of the data.
Data processing speed, database structure, and overall architecture are some of the ways to ensure the infrastructure is optimized to support the big data environment.
One of the other challenges of big data is the fact that many organizations are using multiple systems to manage operations. There might be different technologies for financials, warehouse management, manufacturing, sales, marketing, etc. All these singular systems are capturing data from different sources. Architecture and integration are key to tying all that together.
While the data may be captured in other systems that provide some type of analytics support, they must be integrated so that the data can flow seamlessly throughout the organization. The overall architecture and design of systems, integration processes, and ensuring that the data is flowing between different technologies accurately is extremely important to an effective, big data environment.
It's not just the tangible data that's important. It's also crucial to keep an eye on human behavior. It's that comportment that ultimately can corrupt data. If an end-user goes into the system and inaccurately captures data or manipulates the data in a way that changes the real, raw meaning, that's a problem that needs to be dealt with. It is not just architecture and integration between physical systems, it is also ensuring the user behavior is clearly mapped out.
One last thing to point out is, the reason the cloud has become so powerful is the fact that cloud systems are a central way to capture data with multiple applications. Let's say an organization has five core operational systems being used for different parts of the business. When hosting those in the cloud, the data is being captured in one central cloud location. Suddenly it becomes easier to process and access that data for analytical purposes.
The last thing to think about as it relates to big data is data privacy and security. Considering regulations that dictate where data can be used or stored. As more data is collected, there's an increasing sensitivity throughout regarding data on how it is being protected.
Tight security regarding private information without a breach must be a top priority. Especially in a highly regulated environment like healthcare, government, banking, or financial services. Those are examples of industries where privacy and data security are even more crucial.
In addition to general confidentiality, data needs to be secured. Meaning information cannot be accessed from the outside, and internal employees have limited visibility so that no one comes into violating any security or privacy protocols associated with the data.
These are just some of the things to think about in relation to data. Big data is one of the biggest trends happening in the digital transformation space right now.
For more information on other types of trends that are happening in the digital transformation world, I encourage you to subscribe to my YouTube channel and also download our annual Digital Transformation Report. This is a report that covers digital transformation trends and best practices, as well as independent software rankings and reviews for different types of technologies that might help enable digital transformations.
If you have any questions regarding big data and why it’s so big in the digital transformation space, or if you have additions/feedback, please don’t hesitate to reach out to me directly. I am happy to be an informal sounding board as you move through your digital transformation journey.