Big data predictions: 8 analysis trends in 2020

Memory processing, predictive analysis and data automation will be some of the most popular topics in analysis in the new year.

Image: carloscastilla, Getty Images / iStockphoto

In 2019, business requirements for real-time and almost real-time analysis increased and data continued to expand their role in day-to-day operations and decision-making. Companies will build on these trends in 2020 and that will encourage analysis providers to add new opportunities and expand their offering.

SEE: Cheatsheet: becoming a database administrator (free pdf) (TechRepublic)

Here are eight important trends for analysis in 2020.

1. Processing in memory

The costs in memory decrease and this will stimulate more analyzes to real-time environments. The demand for real-time or almost real-time analysis requires fast CPUs and processing in memory.

Companies want the opportunity to respond immediately to online sales activities, warnings about their production infrastructure or sudden changes in financial markets and portfolios.

2. Processing in natural language

Speech-based applications and analysis have not evolved rapidly in recent years, given the challenges of trying to capture different speech tonalities and accents with accurate natural language recognition. The good news is that natural language recognition, interpretation and mechanics have greatly improved – to the point where more analytical questions can be asked through voice commands.

This is ideal in fast environments such as warehouse yards and logistics and in other situations where employees have to work hands-free. Natural language processing also works well for executives and managers who want to obtain data by using voice commands from their mobile devices.

3. Graph analyzes

Spreadsheets have been very important to involve companies in analysis, but many companies are at a turning point where their data and the complexity of their analysis queries exceed the capabilities of the common spreadsheet.

I predict that graphic analysis will get a better grip in 2020. With graphic analysis, companies can easily determine the relationships between many different data points – even those that initially seem not to be connected. Graphing technology simplifies the task of linking people, places, times and things and can accelerate times to the business insight market.

4. Life cycle development analysis

Companies and IT departments are starting to view their analysis apps in the same light as they look at their traditional transactional apps. IT will develop lifecycle management policies and procedures for analysis – starting with application development and testing and extending it to launch, support, backup and disaster recovery.

5. Enhanced analyzes

Business departments for IT and data sciences will begin to integrate the various analyzes into an organized whole. There is the baseline of rudimentary analysis, and then there is the possibility to extend this analysis with machine-generated data queries through artificial intelligence (AI) and machine learning (ML). Both AI and ML ‘learn’ from data analysis repositories by observing repetitive patterns of data, processing and results and then asking derived questions about what has been learned. AI and ML will increase – not replace – human creativity in terms of creating unique analysis sequences. Because AI / ML can quickly detect repetitive patterns, they can potentially bring faster times to market for certain business insights.

6. Predictive analyzes

In 2019, companies continued to use analyzes to gain insight into historical and current situations. In 2020 there will be a shift to more predictive analyzes to assess future economic conditions, risk areas, climate trends, infrastructure maintenance and investment needs.

7. Data automation

With “dirty data” that costs the US economy $ 3.1 trillion a year and data scientists who spend up to 80% of their time cleaning and preparing data, companies want data automation that can eliminate human involvement in these meticulous operations. This will make the time of data scientists more productive and speed up the time-to-market for analysis, allowing earlier prepared and verified data to be obtained faster.

8. IoT analysis

IoT solutions providers have focused primarily on equipping their own tools with analysis, but companies will want more. In 2020, IoT analysis will evolve towards a more holistic approach. Next year will be a “stepping stone” to unite the flows of IoT analysis, and import companies will step into an integrated IoT grid that better reflects actual business activities.

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