Watch These Data, Data Analytics Challenges and Trends in 2019

Data Analytics Challenges

Businesses are betting on big data analytics as the way forward for success. As a relatively overhyped technology, big data has not yet been fully exploited. With that said, evident are the major roles that big data is playing towards the success of future enterprises.

The revolution has not been an easy one, with industries raising the bar for future data with each coming generation. In this analytics space, which is only a signal of the opportunities to come, we can easily conclude data analytics as the driving force for digital transformation.

Before you therefore flip out your big data screen to reinvent the promise of complex business problems, note that the changes in the industry have just began.

Blending With Internet of Things

The next wave of development anticipates the connection of more than 20.2 billion devices across the Internet of Things Infrastructure. The amount of data collected by this gargantuan network of devices will rise to fever pitch.

To dominate in the interpretation of such data, businesses will need to implement data analytic platforms. 2019 is the time that the world experiences the integration of data analytics and IoT devices. Both of which are an ideal match in the deployment of transparent data collected through millions of interconnected devices.

Amazon expects to rake in more than $18 Billion in revenue by the year 2023 through its IoT software platform that has been integrated with the Amazon Web Service. In fact, the AWS is one of the leading IoT companies in the world both in technology and Return on Investments as of today.

Data analytics and Machine Learning

57% of companies are spending on machine learning. According to a survey by Deloitte, this percentage can be attributed to the opportunities presented by big data. Most companies today view big data as a revenue driver.

In 2017, Google invested in a machine learning system that would help businesses and individuals understand, monitor and valuate their data. Although the analysis of data back then would have seemed trivial, today two years later, the understanding of data models has reached a whole new level.

Currently, businesses are using data analytics to grow marketing campaigns, identify fraud, manage supply vs. demand, analyze customer behavior and make more quantifiable and predictable SEO decisions.

The opportunity for data science vs machine learning will present an ideal match in the world of predictable business solutions.

Augmented Analytics is a Force Behold

A forecast done by information week predicts that augmented analytics will be the driving force of new purchases, data science, informed decisions, prediction of market trends, embedded analytics and business intelligence.

By the year 2023, the global augmented analytics market will have grown from $4.8 billion to $18.4 billion.

It is estimated that the growth will improve by a compound annual growth rate of 30.6%. Qlik, a big data company located in the heart of Pennsylvania is rebranding the face of augmented analytics by deploying business intelligence solutions, agile analytics and governed data discovery.

Python will be the Language for the Future

Python is taking the front lead in the growth of machine learning and data analytics. Although a variety of data science languages are expected to grow in 2019, polls suggest that python is going to overtake R-programing as the leading programming language for machine learning.

It is worthwhile to note that python has been the front runner in the creation of python based data technology, for instance Google created tensor flow using python.

Challenges Facing Data Analytics in 2019

On the downside, various fortune 500 companies are afraid that adopting big data analytics might not translate to measurable results. The worst challenge facing data analytics is the problem of growing data.

Dealing with data growth presents a great deal of trouble, given that the data stored in global information systems is usually expected to double within one year. In most cases, big data is unstructured.

Which means it is composed of an assortment of images, audio, text, documents and video. Another underlying challenge is the need to generate data in a timely manner. This challenge has created the vacuum for the idea of real time data analytics.

Occasionally, the goal of implementing data analytics in businesses is to become more competitive. These capabilities are enhanced by the ability to act on the data and make quick decisions, but this is only possible if organizations can extract data in real time and act on the insights quickly.

Conclusion

With the above said the promise of data analytics has lot of opportunities to offer. Enterprises that seek to stand out among the majority can choose to take on data analytics and make life changing business decisions.

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