11/14/2022 | Press release | Distributed by Public on 11/14/2022 03:23
The Capgemini Research Institute surveyed 500 organizations and 5,000 employees around the world and spoke with academics and executives and found that remote working is definitely the new normal: 75 percent of the organizations expect at least 30 percent of their employees to work remotely, while more than one third expect 70 percent of their workforce to become remote. With such a large portion of the workforce working outside the office, the walking down- the-hall method for gathering data expertise no longer works. Without those in-person interactions, however, 65 percent of workers now feel less connected to their coworkers. Businesses must recreate that connection virtually, especially where data is concerned.
Organizations need to be more agile and reduce decision-making cycle times. To deliver, businesses are enabling workers to make more decisions whenever they are needed. But this requires quick and frictionless access to the right data at the right time. And they also need to trust that the data is current, relevant, and available. Easy, right? No, it's not. Moving a business forward requires fast access to good data for more applications. But that turns IT and analytics teams into the bottleneck. Let's face it, the inquiry model - ask for insights, wait weeks for answers - does not support today's pace of business. That was fine when decisions were made by a few executives at the top of the organization. Today, it's unacceptable.
Leading companies, however, empower the middle of the organization to speed time to market, push digital acceleration, and maintain a competitive advantage. The traditional inquiry model simply does not provide answers fast enough. If you're too slow, you lose time, money, customers, market, and maybe your job.
Enter self-service BI. It drives faster decisions, more innovation, lower costs, transformation at scale, and improved quality, safety, and efficiency. But how to get there, and become a self-service data master?
A more participative self-service BI environment needs to be encouraged. Unfortunately, for many organizations, this change is slowed by internal issues, fiefdoms, and siloed data and systems. But for smart organizations that have sorted out data access and sharing requirements, self-service BI drives data literacy. The goal should be to build a culture where people seek to understand data and its context. Putting data at every business users' fingertips is the essence of self-service BI. Like the concept of a data mesh, self-service holds that people closest to the data - the data producers - should make the data available. Those who need the data - data consumers - can then access it whenever.
For self-service BI to succeed, however, the entire data value chain may need to be fixed. Data has to be easy to find, understand, access, and use for everyone in the chain: data engineers, analysts, data scientists, and business users. Productivity along the chain can be enhanced with a data catalog, which is a repository of metadata on information sources from across the enterprise, including data sets, business intelligence reports, visualizations, and conversations. It makes the data more accessible and understandable to everyone, especially less-skilled data consumers. It also prevents requests for insights that already exist or questions that have already been asked.
One insurance customer determined that just having a data lake didn't by itself generate important business insights. It needed to teach people to fish in the data lake. They needed to move from a culture of analysis to a culture of reporting. A key element of doing so was implementing a data catalog. The company is in the process of using catalog to datanearly 12,000 employees so they can answer questions that drive to the best next action. Who are the customers that I should call today? And what tasks should I complete today? With claims employees, they saw increased efficiency with self-service information. And on the business analyst side, they saw 25 percent time-savings due to decreased data inquiries.
Many technologies are needed to deliver self-service BI. First, though, users need to find the data. That data discovery - and understanding the context of the data you do discover - is critical for the typical data consumer. It's also important to clearly understand when data is not available, and when data is old, incomplete, inaccurate, or otherwise questionable. For this reason, it is not surprising that recent research by Capgemini has found a massive trust gap between the IT-facing arm of organizations and business units. CIO David Seidl says, "as a user, a highly usable data portal or access tool including data discovery and contextualization is critical for more casual, non-power users. I think that's the real destination of self-service BI in the long term."
A data catalog does this all by delivering data discovery, contextualization, and user-friendly tools for casual, non-power users. Specifically, a data catalog enables any user, regardless of skill set, to find and understand data via natural language instead of SQL queries. At the same time, a data catalog provides a business glossary to convert technical jargon, obscure field names, or complex database nomenclature into easy-to understand business terms.
A data catalog enables self-service BI, seamless data collaboration, integrated communication, and the sharing of internal expertise, all built on a foundation of trust. This empowers workers to explore data and discover the answers on their own. And, when they can't, it points them to the resources and people that can help.
This not onlyempowers the businessbut also makes themmore accountable fordriving data-powereddecision making.
The next wave should aim to unlock all the enterprise data using AI. This will empower knowledge workers to explore data before a report or analysis exists, and then drill into data and discover the answers on their own. And when they can't, it points them to the resources and people that can help. This step will sustain knowledge workers in an increasingly hybrid work modality. The combination lets AI analyze data and surface insights while Natural Language Processing (NLP) allows users to build on top of those surfaced insights and ask the next questions. This will give these workers immense power.
An increasingly distributed and digitally enabled workforce needs self-service BI, rather than solely relying on central services.
Those serving themselves need not only access to data, but also to the data about the data (metadata).
A data catalog provides a trusted, empowering foundation for self-service BI.
Artificial intelligence augments current data-catalog functionalities, making self-service BI even more accessible to more people.
Capgemini's Innovation publication,Data-powered Innovation Review | Wave 4 features 18 such articles crafted by leading Capgemini and partner experts sharing inspiring examples of it - ranging from digital twins in the industrial metaverse, "humble" AI, serendipity in user experiences, all the way up to permacomputing and the battle against data waste.. In addition, several articles are in collaboration with key technology partners such as Alation, Cognite, Toucan Toco, DataRobot, and The Open Group to reimagine what's possible. Find all previous Waves here.
We are sorry, the form submission failed. Please try again.