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Top 8 Data Analytics Trends 2017

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Data analysis is rising and this is why you must be aware of Data Analytics Trends to survive the market.

The amount of digital data is growing at a rapid rate, doubling every two years and changing the way we live.

Let’s first understand the meaning of data analytics…

What is Data Analytics?

It is the science of examining raw data with the purpose of drawing conclusions about that information. It involves applying a mechanical process or algorithm to derive insights. For instance, running through a number of data sets to look for meaningful correlations between each other.

2.5 billion gigabytes (GB) of data was generated every day in 2012. (IBM)

Data is growing faster than ever before and by 2020, about 1.7 megabytes of new information will be created every second for every human being on the planet.data-analytics-trends1

Top 8 Data Analytics Trends

Here are top data analytics trends that are going to dominate in 2017:

1. Convergence of IoT, Cloud, and Big Data

There is already substantial progress on the IoT front with primary enterprises in the automotive, manufacturing, and logistics sector. In 2017, everything will have a sensor to send information back to the mothership.

Undoubtedly, the demand for data analytics tools will grow in 2017.

2. Self-service analytics extends to data prep

Self-service data discovery has become the standard, data prep has remained in the industry. However, this will change in 2017.

Common data-prep tasks like HTML imports, JSON, data parsing, and cross-database joins will no longer to outsourced or delegated to specialists. According to IDC report, data preparation market will grow 250% faster than traditional IT controlled tools. Therefore, non-analysts will be able to tackle these tasks as well.

3. Analytics to be Everywhere

Analytics works best when it’s a natural part of people’s workflow. Soon the analytics will become pervasive and the market will expect analytics to boost every business process. With business intelligence, analytics will be in the hands of people who have never consumed data.

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4. IT is the Data Hero

IT will emerge as the data hero who helps shape the future of the business. This will make data literacy one of the fundamental skills of the future.

In 2016, LinkedIn listed business intelligence as the most demanded skills to get you hired.

Also, in 2017, data analytics will become a mandatory core competency for professionals of all types. People will expect intuitive business intelligence platforms to drive decision-making at every level.

5. Prescriptive and Predictive Analytics Tools

This is one of the most discussed data analytics trends among professionals.

  • Predictive analytics

It is the practice of extracting information from existing data sets with an aim to forecast future probabilities. It is an extended part of data mining. With predictive analysis, you can indicate what might happen in the future with an acceptable level of reliability along with risk assessment and alternative scenarios. It includes estimated future data.

  • Prescriptive analysis

It is going to be the future. It examines the content or data to determine what decisions should be made and what steps should be taken to achieve the desired goals.

The techniques used in prescriptive analysis includes machine learning, heuristics, recommendation engines, neural networks, complex event processing, simulation, and graph analysis.

Predictive analysis will help businesses optimize scheduling, supply chain design, inventory, and production etc to deliver what your customers want.

6. Artificial Intelligencedata-analytics-trends

AI is the number 1 chosen by Gartner in their 2017 strategic technology trends report. Artificial intelligence (AI) is the science that aims to make machines execute what is done by complex human intelligence.

Machine learning and AI are revolutionizing the way we interact with our management and data analysis. Businesses today have real-time dashboards to help businesses see what is happening at every second and giving notifications what shouldn’t be done.

Therefore, the demand for efficient data analysis tools is increasing and the arrival of the Internet of Things (IoT) is bringing an uncountable amount of data to promote the statistical analysis and management.

More than half of all large organizations across the world will use advanced algorithms and analytics to be more competitive by 2018. (Gartner, 2016)

AI will be able to understand the data and predict what is upcoming. With this kind of automation, it might transform decision making and managers will need to know how algorithms reach their conclusion and eventually adjust.

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7. Digitization

The process of turning any kind of video, sound, image or analog signal into a digital format that will be understood by electronic devices and computers. This information is often easier to share, access, and store than the original format.

Digitization will transform offline and manual business processes to computer-supported processes and online networks.

As per McKinsey study, the benefits of digitizing information-intensive processes are crucial. They are going to cut the cost up to 90% and there could be a huge improvement in the turnaround times as well.

Therefore, in order to be ahead of your competitors, companies should get Digital Marketing training as well as implement new data sources such as devices connected to the internet, crafting new models to drive new business processes.

8. Visual Data Discovery

The data has reached a volume that is now insurmountable even for data analysts. So much so that, when they step inside the data, even the data specialists don’t know initially where it will lead.

Most of the times, they begin their analysis with visual data discovery to understand the structures or patterns in data sets that seem at first sight incomprehensible. Therefore, the demand for visual data discovery tools and explorational visual analytics tools will increase in 2017 and beyond.

Other Data Analytics Trends That Will Dominate 2017

  • Demand for data science skills among traditional programmers will increase to let them stay effective, employable, and relevant in their careers.
  • Development of cognitive computing assets, machine learning, and artificial intelligence for production deployment.
  • Open source tools focused on embedded cognitive IoT and deep learning will come into data app developers’ core workbenches, Spark, R, Hadoop, supplementing, and extending.
  • Data analysts will hold operational responsibilities that will focus on deploying monitoring and managing real-world experiments, focus on designing, A/B tests, predictive analytics, and machine learning inline to core business processes.
  • Data scientists will work within multidisciplinary, integrated cloud-based development environments that incorporate standardized notebooks, robust security controls, rich collaboration and project tracking tools, and composable containerized microservices.

The businesses or traditional programmers who are unable to keep up with the upcoming trends, Data Analytics Course that offers clarity of concepts and practical knowledge will help you survive the market!

Sakshi is a content marketer during the day and a reader by night. She writes content sprinkled with a twisted imagination. She has done her graduation in psychology from Delhi University and has an insane love for history.
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