- Data management
- Data Analytics
- Internet of things
- Artificial Intelligence
- Software Factory
- Customer Experience
- Data-driven ESG
- Case Studies
We probably all remember how it was when you wanted to buy a table lamp, for example. You had to through several shops until you finally picked the one you liked. And now? In just a few minutes online, it’s evaluated, selected, ordered, and paid for…. Over the last two decades, the Internet has changed the way common people consume information, search for goods, and communicate with their surroundings. Every new generation that enters the corporate world is therefore less tolerant and willing to wait for information. Meanwhile, data sources are constantly expanding, and the time it takes to analyze them is getting longer and longer. And that places tremendous pressure on IT.
Today, the story of a customer buying goods or ordering a service is totally different. From finding out about other users’ experiences to getting recommendations from an Instagram influencer, each customer leaves a digital trail behind them. Companies and their IT departments are under a lot of pressure to use these data and the information they contain to analyze various customer impulses and, ideally, to do so in no time at all. They cannot afford to wait for a response, a report, a few days or weeks to learn that the new campaign has taken off and the goods are already almost sold out.
The companies that are able to analyze data, provide interesting information, and thus support and improve decision-making at any business level will be more effective, competitive, and profitable.
Traditional methods of analysis
Let us imagine a situation in a supply chain. A team of data specialists prepares a standard weekly or monthly report for the managers. Given the volume of data and the amount of time they would have to invest, they apply the well-known 80/20 rule, and focus on the top and bottom 20% of products. Because the rest of the data is not really analyzed, the company is robbed of profit it might otherwise have been able to generate.
What if a manager comes in and wants to understand why, in the last quarter, there has been such a drop in the sales of toys for children under 3? This kind of question and others like it are handled with ad-hoc investigations carried out by a data exper1§t. At best, this takes hours, at worst, days. In such a situation, the manager relies on intuition. By the time the report finally arrives, it is possible that the manager has already had to make a decision, or that sales have already moved in another direction. The information is thus irrelevant.
In the long run, this prompts the business department, full of frustration, to hire its own data analysts, build duplicate data sources, and implement its own analytical tools. For the IT department, this situation is unsustainable.
Search-driven analytics with elements of Artificial Intelligence
Just as modern search engines have changed the way we currently buy goods, look for information, or find entertainment, we now have the option to use analytical tools that work according to the same principle. We can ask a simple question like “What products were bestsellers in the second quarter in Central Bohemia?”. And the result is available immediately, within a few milliseconds.
The companies that focus, in the near future, on speeding up data analysis will give their employees a great advantage. Let us take a look at the example of millennials, who are starting to make up a large percentage of the workforce. They certainly do not want to wait for anything. They want their analytics app to work in the same way as their other favorites, such as Google. They want all the information to be available with a single click.
Making work faster and more attractive
Next-generation analytics use an in-memory database and a search engine tailored to analytical operations. This dramatically improves query speed. This kind of solution combines all forms of data, both structured and unstructured, from the data warehouse, from the cloud, and from users’ tools, for example, in the form of an Excel file. The data is then updated every time the application is opened. The greatest benefit for workers using this type of tool is the opportunity to develop, edit questions, ask additional questions, add details… and see the next connection immediately.
If a businessperson handling, for example, pricing in the supply chain had such an application available, they would no longer have to wait for a report. Instead, they could use the time to analyze their whole product line and make optimal decisions more efficiently, which would result in lower costs and higher turnover. And that’s not all. The businessperson could also analyze the direct relationship between a product’s sales and its reception on social networks, and in different regions at that.
Another advantage is that even less technical people are able to use search engines without the help of a data analyst or special training. They are able to analyze data in much the same way as they search for goods or information in everyday situations.
Business comes first
It seems that analytical tools have long tried to meet IT requirements such as quality or governance. But that is no longer enough. The companies that want to gain a competitive edge and dominate on the market must take their staff’s needs into account. They need to provide them with tools that will enable them to work with information more efficiently, rather than oblige them to spend time learning how to use new technology.