Big Data Future - 【2020】
future of big data

Big Data Future

Meanwhile, it has become clear to everyone that data analysis is more important than ever. Many companies have already begun to reap financial and strategic benefits from data-based decisions, so can you afford to wait? What changes are already predictable and what are the major trends? – Big Data Future

Trend 1: The Time for AHA – Amplified Human Analytics and Citizen Data Scientists

The best software and the most powerful hardware are useless if there is no one who can use them properly. The human component and the ability to think abstractly, associatively and intelligently are and remain irreplaceable, if only because every company is positioned, managed and controlled by people.

Companies that intelligently combine new technical possibilities with the human component and operate Amplified Human Analytics are looking to the future – like Adidas, for example. In order to better meet customer demands in e-commerce while reducing operating costs, the company developed an easily customizable forecasting workflow for its own online sales.

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The sporting goods manufacturer compares current and older data points and weighs them individually according to, for example, the product line and/or e-commerce channel, in order to calculate a trend forecast and the expected increased demand. The result: more satisfied customers in 17 different markets with reduced workloads, more targeted distribution of goods and lower costs for the company.

As shown again here, the combination of technology and human resources also provides the solution to the shortage of highly qualified data scientists. After all, not all data analysis tasks have to be performed by the few data experts. Meanwhile, software as a service tools exist that are relatively easy to use and free of code, so they can also be used by trained personnel.

Gartner, a leading market research and consulting firm, has coined the term Citizen Data Scientists for this, employees from specialized departments who are trained in data analysis. Using free programming software and preconfigured R and Python tools, they can independently examine the data sets, establish their own analysis workflows and models, and replicate them over and over again.

An important advantage is that citizen data scientists bring with them the respective expertise of their department and can better understand and directly apply the results of the analysis. This leaves the highly specialized data scientist more time to deal with the really tough nuts and creative analyses, while individual departments can help themselves.

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Trend 2: Companies need more data culture

To be truly successful, competent data management must not be limited to individual departments. Instead, every company in the 21st century must ensure that an open and positive culture around the whole issue of data analysis permeates all areas of the company, because data is created everywhere. At the same time, this also lowers the inhibition threshold for employees to deal with data analysis on their own, the topic becomes more tangible and finds its place in the various teams.

Workshops, cooperation platforms and internal contact persons are very useful for overcoming barriers, breaking down prejudices and guiding employees. At the same time, Citizen Scientists from big data should also be part of Data Science Teams, as this adds departmental knowledge and avoids tunnel vision. They can also ensure that analysis reports and results are correctly communicated throughout the company. This not only contributes to the general acceptance of data analysis in the company, but can also become a competitive advantage for companies, for example by actively promoting training to become a data expert, which definitely increases the attractiveness of data analysis as an employer for applicants.

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The first signs of this trend can already be seen today: According to a recent IDC study commissioned by Alteryx, data has become the heart of the digital transformation. For example, 80% of the companies surveyed now process data in a structured way on several process levels and use the results in different areas of the company.

Trend 3: Machine learning and artificial intelligence – the next big thing?

In addition to big data, artificial intelligence (AI) and machine learning (ML) are also currently occupied by the media, politics and business, although AI is often more of a laboratory than a practice. However, both are already used in data analysis and will become increasingly important in the future.

AI and ML meta-technologies open up new possibilities: Cognitive Analytics, Contextual Insights and Augmented Analytics are keywords that we will hear more often in data analysis in the future. The promise is that data analysis will be even easier for everyone, from data preparation to data discovery and understanding.

Systems based on machine learning and AI are able to process already collected data and new data in a linked way and to “learn” from this experience. This allows for reliable, consistent and goal-oriented analysis of large amounts of data. It is essential that the “training data” of the algorithm are of high quality, otherwise the results will be distorted later. Therefore, for companies, it can already be profitable to invest in the LMA and AI, because these empirical values are already a great advantage and save time and money.

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But it also works in a much smaller way; many repetitive and monotonous tasks, such as data entry and access, can be automated, giving employees more capacity for more complex tasks. According to Gartner, more than 40 percent of data science tasks will be done this way by 2020 – a huge potential for labor and time that companies can free up in their own employees. Automation will also reduce the number of errors because fewer human errors occur, even in very complex calculations.

Therefore, the trend is definitely to look at the ML and AI already and bring them to the level of top management in companies today. However, it is essential to understand the data and how to use it, as well as a careful plan to implement the processes and systems.

By the way: with all technology, the human component should not be ignored either. After all, it is the employees who ultimately decide how and whether machine learning and artificial intelligence are applied.

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Taking the bull by the horns and applying the trends

When we imagine beyond 2020, companies will be shaped by three decisive factors:

1. Amplified human analysis, where technological possibilities and human capabilities complement each other profitably

2. A holistic and open data culture throughout the company with balanced teams of highly qualified data scientists and citizens.

3. The sensible use of the LFA and AI for automated processing of large and complex amounts of data, which frees scientists from long and repetitive tasks and allows them to concentrate more on creative solutions to complex problems.

One thing is certain: Data analysis needs structure and strategy, staying power and a bit of stubbornness, at least in the beginning. So why not start now? Only companies that recognize this will be successful in the future of tomorrow’s global competition. After all, no matter how long the road may seem, in a year’s time you will be happy with the steps you have already taken today.

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