Predictive analytics and data automation
INTEGRITY BEAT - Henry J. Schumacher (The Freeman) - January 17, 2020 - 12:00am

Predictive analytics and Data automation will be some of the hottest topics for business in the new year.

As potential disruption of our business remains on the agenda, demand for real-time and near real-time analytics will continue to rise. Data will continue to expand its role in everyday business operations and decision-making.

Here are seven key trends for analytics in 2020:

1. Real-time processing - The demand for real-time or near real-time analytics will require fast CPUs and in-memory processing.

Companies want the ability to instantaneously respond to online sales activities, alerts about their production infrastructures, or sudden changes in financial markets and portfolios.

2. Graph analytics - Spreadsheets have been instrumental in getting companies engaged in analytics, but many companies are at an inflection point where their data and the complexity of their analytics queries are surpassing the capabilities of the common spreadsheet.

Graph analytics will gain traction in 2020. With graph analytics, companies can easily determine the connections between many different data points--even those that at first do not appear to be connected. Graph technology simplifies the task of linking people, places, times, and things, and can speed times to market for business insights. We are offering training along these lines…

3. Analytics life-cycle development - Businesses and IT departments will begin to look at their analytics apps in the same light that they look at their traditional transactional apps. IT will develop life-cycle management policies and procedures for analytics--beginning with application development and testing, and extending to launch, support, backup, and disaster recovery.

4. Augmented analytics - Corporate IT and data science departments will begin to integrate the various pieces of analytics into an organized whole. There is the baseline of rudimentary analytics, and then there is the possibility of augmenting these analytics with machine-generated data queries through artificial intelligence (AI) and machine learning (ML).

Both AI and ML “learn” from data analytics repositories by observing repetitive patterns of data, processing, and outcomes, and then posing derivative queries from what is learned. AI and ML will augment--not replace--human creativity in terms of framing unique analytics queries. Because AI/ML can rapidly perceive repetitive patterns, they may be able to deliver faster times to market for certain business insights.

5. Predictive analytics - In 2019, companies continued to use analytics to gain an understanding of historical and current situations. In 2020, there will be a shift toward more predictive analytics to assess future economic conditions, risk areas, climate trends, infrastructure maintenance, and investment needs.

6. Data automation - With data scientists spending up to 80% of their time cleaning and preparing data, companies want data automation that can eliminate human involvement in these painstaking operations. This will make data scientists’ time more productive, and speed time to market for analytics, which can obtain properly prepared and vetted data sooner.

7. IoT analytics - IoT solutions providers have primarily focused on equipping their own tools with analytics, but companies will want more. In 2020, IoT analytics will move toward a more holistic approach. 2020 will be a “step off” point toward unifying the streams of IoT analytics, and input companies are getting into an integrated IoT grid that more closely reflects actual enterprise operations.

Looking at these seven key trends for analytics in 2020, it becomes obvious that companies have to train their staff in data analytics so that they can respond to market opportunities faster and – at the same time – avoid disruptive innovation affecting their business by competitors.

We have to be aware of the fact that the Philippine labor force still lacks employees with Data Science and Analytics (DSA) skills, according to a study released by the Philippine Institute for Development Studies (PIDS) just now.

The findings show that the country has an undersupply of functional analysts and data stewards. Training of data engineers and data scientists in that direction becomes essential.

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