How New BI Tools Can Help Shape Decision-Making
Information insight is one of the most crucial elements in helping companies keep pace with rapidly changing business environments. Business Intelligence (BI) is complex with a lot of moving parts needed to get results. Information insight is key for the ability to know more than what just happened, but also what will happen next.
For many businesses, the biggest barrier to using analytics isn't its value, but how to access and understand data for better decision-making. To move to a next-gen BI mindset, you need to know about two cutting-edge concepts: unstructured data processing and digital twins.
The Evolving BI Landscape
Using unstructured data inside of traditional BI tools is a long-term trend that goes back more than 15 years — before most companies even knew what big data was — when vendors first discussed the use case to make this scalable for the business and the end-users. Even now, only a fraction of all companies use advanced analytics, and more still have hand-crafted and cumbersome spreadsheets.
The first generation of BI solutions was designed around the needs of corporate data warehouses, and they remain a vital part of the business intelligence landscape. But with the growth of unstructured data in multiple different sources — like document and content management systems, CRM systems and many others — a human being can simply no longer grasp the amount of data that organizations manage, so they need tools.
With the rise of applied AI, these tools have become even more necessary. Advances in deep learning, reinforcement learning and other AI techniques are triggering a major shift in the way companies approach everything from day-to-day business operations to strategic planning. For businesses, AI promises to make work easier and more efficient, freeing up valuable time and resources.
The Hidden Power of Unstructured Data
Using business facts contained in structured — and especially unstructured — information opens up many new possibilities. Literally, every functional area inside an organization like maintenance, research and development, marketing and sales can benefit from this information as it helps them make better decisions and gives them a competitive advantage.
While most traditional analytics tools have typically relied on structured data (like what's found in a database), intelligence gathered about other critical pieces of data is largely found in unstructured environments (such as agreements, tickets, notes or emails). Platforms that contain unstructured data have fundamentally shifted how people consume information. Want to know what your opponents are up to? Check their Facebook and Twitter accounts. Want to know what your customers are saying? Read the reviews. Most of the time, the glut of excess information that businesses struggle with is from unstructured data. The problem? A lot of it is not easily parsed.
So how do BI reports start being intelligent and provide actionable information? With the goal of business process transformation in mind, business facts contained in unstructured data have the answers executives need. As companies become more data-driven, they're beginning to realize that quality is essential for proper analysis.
Are companies arriving at higher quality in their data? Yes, by way of digital twins.
Digital Twins Are Shifting Product Management
Digital twins are a digital representation of physical assets, processes or systems representing real-life objects, people or services. They capture the 3D geometry, mechanical functionality and electrical, and visualization components that go into making a product. When combined with an Insight Engine, they can also include all the data around a product's history, maintenance logs, spare parts availability and purchasing records. The concept of digital twins has emerged as a holistic view of data through acting as digital replicas.
Businesses are shifting from traditional product management to a more holistic view by using knowledge management to create digital twins for every business-relevant information object in the enterprise. Digital twins are now regularly adopted in new product and service development and, in many cases, are supplanting traditional product management methods. This technology combines data and machine learning with a new level of connectivity to people, processes and things.
Digital twins are constantly updated to reflect any changes made to the real-life version, which makes them highly valuable to companies. They provide the information needed to determine whether a product is in stock or not and where it is available for purchase. Better product management can be realized by analyzing how individual components work together, rather than looking at components individually.
How To Decide About Digital Twins
The ultimate goal of these AI-driven processes is to have the impact of delivering instant info, but, as a business leader, there are a few things to know about your existing set-up and a few challenges to be aware of.
When taking a look at the needs of each department and functional area, it's important to take into consideration the types of information views that workers need right away. For example, scaling the knowledge of lessons learned from projects throughout an organization can be of great value, so it is critical to enable this with specific parameters and dashboard views.
When considering adopting digital twins, there are many benefits to both processing unstructured data and using digital twins, but also a few instances where it might not be the best solution. It's important to weigh both. Consider both the size of your organization and the complexity of the objects or expert knowledge that needs to be searchable. While many companies are still exploring the possibilities of incorporating digital twins in their business, others are already seeing the benefits.