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  • Writer's pictureTony Paul

7 Key Data Analytics Trends Predicted to Dominate 2017

Updated: Feb 12, 2021

In this era of humongous data production from every industry, analytics has become the holy grail for growing your business. According to statistics incurred by Infosys, by 2020, global data will grow up to 40 zettabytes, with machine-generated data being projected to increase by 15 times. 

As Peter Sondergaard rightly quoted,


“ Information is the oil of the 21st century and analysis is the combustion oil.”

Today, data has reached to an unaccountable magnitude which in turn calls for creating more meticulous ways of data analytics. We have come a long way, from Spreadsheets to interactive business dashboards. We are rising in the arena of insightful data visualization and are

progressing towards the golden age of democratizing the data using self-service tools.

Nowadays, companies are seeking instant “real time” insights. They wish for exclusive customization for greater customer retention. The realm of BI is expanding in new horizons

which is bringing exclusive innovations in its tools.

2017 will be the year where we will tap the potential of collaboration and cloud analytics. It will kick in the possibilities of Visual Analytics: embedded analytics is all set to revolutionize BI and a lot more. Listed below are 7 key data analytics trends which are predicted to dominate 2017:

1. Predictive and Prescriptive Analytics

2016 has been the year of predictive analyticsIt offers endless virtues to any organization and paves a way to improve their business processes. It enhances decision making by accurately assessing future opportunities and risks. Consider it a gateway to gain a better understanding of customer behavior which aids in identifying and evaluating new market opportunities. Predictive analytics is a major catalyst for target advertising which makes it possible for online retailers to provide a personalized experience to their customers and maximize sales. Amazon suggests products and services to users on the basis of their shopping history which in turn is responsible for  30% of Amazon’s sales. This fact speaks volume about the massive advantages of predictive analysis.

In addition to predictive analytics, what’s now emerging in 2017 is Prescriptive Analytics.

Consider it as the third phase of business analytics, right after descriptive and predictive analytics.

Prescriptive Analytics, in short, is a holistic term for varied types of analytical techniques which enhance decision making. This form of analytics aims to forecast what the impact of future decisions would be in order to alter the decisions before they are actually implemented. It helps you to optimize scheduling, production, inventory and supply chain design to deliver what your customers want in the most optimized way.

How Prescriptive Analytics work

As Willy McColgan once said, “Application of algorithms is not a substitute for robust investigational methodology or common sense,”. Adhering to this, prescriptive analysis diminishes the uncertainties of predictive analysis by combining the probability with human-generated results.

2017 shall witness a greater rise in predictive and prescriptive analytics in growing businesses.

It has been estimated that by 2020, predictive and prescriptive analysis will attract 40% of enterprises. By 2019, they will attract a market worth $1.1 billion. 

2. Artificial Intelligence

The pace at which Artificial Intelligence is being embedded in existing technologies is dandy. 

In fact, an estimation made by IDC states that the AI market will grow from $8 billion in 2016 to more than $47 billion in 2020. 

The merge of big data with AI is remolding the way companies garner business value from their data and analytics potential. “People are talking not just about big data, but how we need that data and make decisions,” says Anand Rao, partner and innovation lead for PwC. This statement firmly establishes the significance of AI in the coming times. Firstly, the amount of data production happening around is huge. We produce 2.5 Exabytes of data every day which is estimated to reach 44 zettabytes by 2020. A handful of data analysts isn’t enough to draw meaning out of this data.

The merge of big data with AI

The merge of big data with AI

This is where AI steps in. With such large scale production, AI comes as a solution to speed up the process of analysis and thereby fulfills its ever growing demand. It automates manual operations in a faster way. Thus, enabling us to work with gigantic sets of data without any restrictions.

Along with this, is the ever growing demand of real-time data analysis tools. It has been predicted that more than half of all the large companies will make use of advanced analytics and algorithms to stay ahead in the game by 2018. AI will be the powerhouse of these operations. It will not only be used to interpret the data but also to forecast events. Through AI, machines will operate autonomously and independently without manual operations.

According to Forrester’s Research estimates, 2017 will witness a 300% rise in investments in artificial intelligence. It will be interesting to witness what other doors AI will open up in the sector of data analytics. Its popularity resides in its power to easy down the process and making it efficient at about the same time.

3. Cloud Analytics

Cloud Analytics

In 1977, Kel Olson, the founder of Digital Equipment Corp said, “There is no reason why anyone would want a computer in their home.” What happened afterwards was the evolution of home computers to tiny mobile computers in our pockets! The same goes for Cloud Analytics, inception of Cloud Analytics happened during a time when we were  not equipped to tap its potential to the fullest. But with development of Saas apps and cloud based services and apps, it is on a roll to become the next big thing! The eye popping reality is that the cloud analytics market is expected to grow from $7.5 billion in 2016 to $23.1 billion in 2020.

Companies, irrespective of their sizes are opting for Cloud Analytics. One of the biggest perk is gaining the flexibility to scale up and down in accordance to what your company demands.

The cloud analytics market gets further divided on the basis of the solution it offers. To name a few, they include cloud BI tools, hosted data warehouse solutions, complex event processing, enterprise information management, enterprise performance management, governance risk and compliance, analytics solutions etc.

Data from existing warehouses doesn’t suffice the need of the hour. This is where Cloud Analytics comes forward as a savior by providing an easier solution to tap data from diverse web applications. Google docs, Google Analytics are some of the popular cloud based application that need to be unearthed to gain valuable insights.

However, prominence of cloud analytics is set to grow further in 2017.

4. Embedded Analytics

Almost 90% of UK and US application decision makers intend to invest more in Embedded Analytics in 2017. This year will also witness more products by analytic companies crafted in a

way that can be easily embedded and manipulated internally and externally within the application.

“Embedded analytics refers to consumer-facing BI and analytic tools that have been integrated into software applications, operating as a component of the native application itself rather than a separate platform.” Now this is perhaps the most widely used definition for Embedded Analytics.

Let’s fractionate it further.

Fundamentally, it means that analytics are embedded as an inherent/natural part of the application.


A significant advantage of Embedded Analytics is that it overcomes the drawbacks of BI Tools. Contrary to BI Tools, Embedded Analytics functions in a way that business users don’t have to

exit their business application to view results. Right within the application itself, users can check results, analyze performance and view suggested actions. So, it not only overcomes the need to switch but also gives ample amount of time to take actions. Holistically, it speeds up the entire process. In addition, it also effectively tackles the user adoption issue that is often associated

with data discovery tools.

Embedded Analytics is not a new player in the arena of data analytics. For past three decades,

it has been used to integrate charts, reports, dashboard etc. Today we see its widespread use in

self-service, predictive and blended analytics. It even functions in the cloud where companies

can rent the software for varied periods of time.

5. Visual Analytics

visual analytics

The amount of Big Data generated every passing second knows no bound. For a data scientist, work starts with finding some form of initial lead from the huge bulk of information. The inception of their analysis starts with “visual data discovery.” It is used to find patterns and structures in data sets to derive meaning out of them. Data visualization tools are used to discover multiple relationships and also to allocate varied data sets for further analysis. In short, you get real-time valuable data insight which acts as a catalyst to take quick decision.

The second component of Visual Analytics is the explorational Visual Analytical tools. Use of visualization and visual perception explorational tools helps you to completely unearth big data. Innovations like in-memory processing and combination of multiple information sources, enhance business agility and self-service BI.

6. Collaborative Analytics

collaborative analytics

“Alone we are smart, together we are brilliant.”– Steve Anderson

This quote truly depicts the power of collaboration. Collaborative analytics personifies the brilliance of the same. The Aberdeen Group’s most recent report on Collaborative Business Intelligence (BI)– Collaborative (BI)- Harnessing the Extended Enterprise to boost productivity– claims Collaborative BI deployments have the ability to improve productivity and visibility across the breadth of organizational operations via enhanced knowledge sharing.

The basic phenomenon of Collaborative Analytics is to widen the decision-making process beyond the company boundaries through mutual cooperation and data sharing with the other companies and organizations. It is a combination of collaboration tools, including social media and other 2.0 technologies, with business intelligence software. In the past, BI apps focused on serving individual companies but for collaborative analytics infrastructure, it has to be architected to serve a network of companies characterized by semantic and lexical heterogeneity.

Dresner’s 2016 Collective Insights report suggests that 65% of respondents see collaborative BI as a critical or very important priority for their business. Despite such statistics, Collaborative Analytics till now has not become a mainstream concept. Big players need to advocate and foster this concept. A prerequisite is to acquire certain BI tools like easy-to-use dashboards etc. to facilitate this transition. If development on these lines continues to grow then in 2017, soon we will see collaborative enterprises outgoing individual entities.

7. Data Governance and Business Intelligence center of excellence


Data governance provides us with defined “Data Policies” i.e. rules to integrate security and quality. It formulates concrete “Data Standards”, which acts as a rule book to monitor what to do and what not to do in regard to data. Moreover, it acts as a medium to address data related issues in the sectors of quality, mining, security, privacy etc. Enhanced data quality and efficient access are some perks that come in handy with the application of Data Governance. However, the most valuable application derived from it is the treatment of data as an asset.

Despite all the advantages, a lot more investment is required in order to make Data Governance a success. The urgency to use it gets highlighted by the fact that in January 2016, more than 47 state governments passed laws that require businesses, information brokers, and government entities to implement security measures and in some instances, publicly disclose any security breaches that result in the compromise of personally identifiable information.

In 2017 we will witness Data Governance shifting its top priority to privacy and security.

The BI center of excellence offers a similar solution to the problems addressed by Data Governance. A Business Intelligence Center of Excellence (BI COE) concept was developed to provide governance, development, and application of standards and best training practices and education, related to the deployment and existence of Business Intelligence solutions across the enterprise.

COE through tools like online forums and one on one training empower laymen to induce data into their decision making. It is a systematic way to bring together people, processes, and technology.

It further facilitates change management and an interaction between different geographies and cultures.

2017 will bring a more standardized outlook towards Data Analytics and its management.

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