We will discuss how to use data on the organizational level so more people can get help from data analysis.
The data hunger is excellent; nobody says no to extra information. The only issue is that organizations often don’t know how to transform data into actionable insights once they start collecting data. During our last Innovation Thursday webinar, we discovered what it takes. Get started to realize your data’s potential, which is strategically important in a data-driven world.
Not knowing the best method to read, understand, and turn data into intelligent actions can cost a business dearly. Lost opportunities, lower efficiency, productivity, quality problems, etc., make no one happy. Yet about 60% to 73% of all data within a company would go unused (Forrester).
It is a shame that the stars have never been so good for Smart Data. A recent report by McKinsey shows that 58% of organizations are exploring the possibilities of Smart Data in almost all sectors. Underlying trends such as the Internet of Things, cheap data storage, and the immense cloud computing power ensure that companies can fully utilize the added value.
When surveying those organizations about what they want to use intelligent data for, we see many different applications. Frontrunners improve current products and services, optimize business processes, and automate tasks to give employees more creative space. Businesses can create new value in almost all areas of an organization with the proper management and use of data. Value creation is not the only advantage; companies also see their revenues increase and their costs decrease simultaneously by using data better.
Where to Start? Discover the Data-Driven Platform
How do you start turning data into actionable insights? First and foremost, it is required to understand how such a process works and what it takes. We speak of a data-driven platform if all these steps are combined. During the webinar, it became clear that about 60% of the attendees were still looking for this.
In short, it is about a platform that receives data, which processes, reworks, combines, and enriches it to generate results/output ultimately. The network and its components create a picture of the significant steps your data can (but not always have to) go through. If we zoom in further, we distinguish from left to right the different ‘silos’ through which data moves. It is important to note that these are not actual silos; they often overlap or even partially overlap.
For example, we first look at data capture. From where does data come? Which existing data sources are available? Is data delivered live? Is there batch processing? What about unreliable data sources if a sensor fails, for example? In short, which qualitative data is valid?
Afterward, or simultaneously (remember: no silos), you look at the storage options. Is it about data that already contains a lot of structure (for example, users can transfer this to relational databases), or just not? Is it textual data? Video? Audio? Scannable documents? All these kinds of questions will impact how you construct your data platform. Usually, there is no wrong answer, and different data stores are combined.
The data is stored correctly and securely. And what now? Now, you can get started effectively. Many people see this as the black box of the process; it is a more than necessary step, but it remains very technical and, therefore, less susceptible. Data engineers are working with machine learning and data mining models. They pour specific business logic into formulas and models; in this way, they build an intelligent layer on top of the existing data.
The final interpretation occurs through dashboards (real-time or not) and visualizations using Microsoft Power BI reporting. Think, for example, of reports with analyses of the sales figures or cluster analyses of the customer base. You can also feed systems with these insights and automatically act based on those results.
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