The rapidly accelerating advances in technology, the truly established value of data, and the increasing data literacy are changing what it means to be “data-driven.”
There will be seven major characteristics that will define the data-driven enterprise and corporation in the coming decade. We are already seeing many companies exhibit at least some of them, with more companies to follow suit.
Evidence shows Data-driven companies are 58% more likely to beat revenue goals. Those companies are willing to take the data-driven approach the fastest to capture the most value from data-supported capabilities.
What is Data-Driven Decision Making?
Data-driven decision-making is utilizing your data to inform your decision-making process and validate a course of action before committing to it. In enterprises and large companies, this is seen in many forms:
- Collect survey responses to understand what kind of product or service would the end customer require
- Conduct User Testing to identify potential problems with a product or service offering before the official release
- Launch a new product or service in a test market to test the waters and understand how a product might perform
How data will be incorporated into the decision-making process will depend on several factors, such as the business goals and the types and quality of data you have access to.
With every passing day, more and more data is being produced by SMBs and large-scale enterprises. From a numbers perspective of more than 2.5 quintillion bytes of data each day, it’s never been easier for businesses to collect, analyze and interpret real-time data and make informed decisions accordingly.
So what are the top seven trends and opportunities powering the future of business:
More Data increased data diversity drive advances in processing and the rise of edge computing.
It won’t surprise any corporate entity that the pace of data generation continues to accelerate. Much of this data is not generated from the transactions that happen in databases but come from other courses, including but not limited to smart devices such as smartphones, cloud systems, and video streaming.
The available data is largely unstructured and, till the recent past, was left largely unprocessed and unused by organizations, turning it into so-called dark data.
The need to handle the data generated is moving to the devices themselves, as industry breakthroughs in processing power have led to the development of increasingly advanced and modern devices capable of collecting and storing data on their own without taxing network storage and computing infrastructure. As an example, mobile banking apps can even handle many tasks for remote check deposits without sending images back and forth to the central bank for processing.
In a sign of how things are coming together, a survey managed this year by TechTarget’s Enterprise Strategy Group division showed that the top priority for organizations to support their data initiatives are advancing the use of next-generation technology, moving data from legacy systems to ensure the ability to handle large amounts of data as and when it is generated.
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Data Embedded in every decision, interaction, and process
In 2022 and beyond, nearly all employees need to leverage data regularly to support their work. Medium-sized companies and large corporations can no longer have long year-on-year roadmaps that they can’t tweak based on data. They need to be empowered to make innovative decisions using data techniques that could resolve challenges in hours, days, or weeks.
Currently, more and more organizations are capable of better decision-making and automating day-to-day activities. With more and more automation coming in, employees can focus on more human domains such as collaboration and innovation.
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Data is processed and delivers insights in real-time.
People expect to receive an immediate response in the current day and age. Real-time communication requires decisions through real-time insight to gain a competitive picture of what’s going on within your company. If your organization relies solely on reports, you won’t be able to fix problems that could negatively impact your bottom line. You could be missing out on key insights affecting your business.
This can be seen through real-time analytics examples. A company is reviewing their conference calls from the previous month and noticed 3 of the calls experienced bad quality. It would be difficult to ascertain how many users and individuals were affected on the surface. If, however, you were to look at the time analytics and the deeper level data available in real-time analytics, you’ll see the call right as it happens. You may see some commonality between, say, 100 people. After a further deep dive, you find out that 50 of them are in the same location, 10 of them with the same network switch. The situation can be identified in real-time and fixed sooner rather than waiting for the consequences. The benefits of real-time data analytics can help businesses meet their needs in real-time.
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The Dawn of Data Ecosystems
As businesses increasingly rely on data to make strategic decisions, companies across all industries have embraced data lakes: huge repositories of raw data that they can query to help answer questions and gain competitive advantages.
Thanks to data lakes, companies don’t need to answer all the important questions like “why” in advance when gathering and storing data. Instead, the question transforms into “why not.” It’s a complete paradigm shift in thought process and approach—the more the data, the better the prospects of a healthier ROI.
But even though the use of data lakes has spread quite fast over the past decade or so, they are already showing signs of obsolescence. As privacy regulations become the new norm and cybersecurity scares spook customers and investors, the difficulties and downsides of data lakes are becoming impossible to ignore.
So how do Data Ecosystems help? Data Ecosystems are interconnected, seamless networks representing the next phase of data management. This allows companies with common interests to collaborate around the information dynamically. For example, a bank would have the capability to access and confirm the employment details of someone applying for a mortgage without needing to call their employer or see private details beyond what’s necessary.
The downside of traditional data lakes will continue to cause them to fall out of favor. In the meantime, forward-thinking companies realize that it’s high time for enterprise data to shift toward the latest trend in data ecosystems.
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Integrations are a major hurdle.
An emphasis on security has led to somewhat of a mini data explosion, as security monitoring tools generate large data sets that are analyzed for threats. Security is, however, just one of a dozen data sources that IT is trying to cut across multiple applications, databases, and business systems.
The number of data sources is compounded by the silos of data in the individual business units, departments, and locations. This creates significant challenges are far as integration is concerned, ranging from varied data formats and storage requirements to time-consuming, manual data imports and exports.
Digital transformation efforts depend on being able to integrate data from multiple data sources. CXO-level IT Strategy leaders need agnostic analytics concerning data sources, platforms, and data management.
Data fabric will become the foundation for the distributed enterprise
As digital businesses expand rapidly and remote work becomes the norm, it creates a complex and diverse ecosystem of devices, applications, and data infrastructure.
The data infrastructure can scale on-premise, single-cloud, and hybrid cloud implementations spread across regional boundaries. On top of that, there are many choices for data infrastructure that many organizations are adopting, such as data warehouses, data lakes, etc. Still, there is no single solution to sew all these layers together.
Large-scale organizations realize that no single uniform solution can fit the bill. Therefore, enterprises are driving a rapid transformation towards data fabric architectures, a technology that knits together various data repositories across cloud and regional boundaries in a seamless fashion.
In 2022 and beyond, organizations will create a data fabric to drive enterprise-wide data and analytics to really and visibly automate data integration, data preparation, and semantic tools. Such an architecture will allow organizations to reduce the time to delivery, making it a preferred data management approach in the coming year.
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Embedded Analytics Continues to Grow
Embedded analytics is on the way up in the corporate world. Embedded analytics is a relatively new type that embeds analytics and dashboards into your software. Where business intelligence software is a standalone piece of software, embedded analytics is a component of other programs that help enable analytics.,
For example, DotNet Report Builder provides its state-of-the-art embedded reporting and dashboard solution, allowing users to manage their Ad hoc reports and dashboards by embedding the builder in your application. The suite lets you take the developers out of it and let your customers create their reports. The easy and intuitive report builder lets your clients choose their fields, create their filters, and even schedule their reports, all from inside your application, freeing up your development team!
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