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Advanced Analytics: Exploration of Some Transformative Future Trends

By   /  August 29, 2018  /  No Comments

Advanced AnalyticsThe wide availability of streaming devices, monumental volumes of data, cheap storage platforms, and hosted Analytics Infrastructure have jointly contributed to the rapid adoption of Advanced Analytics by businesses of all shapes and sizes. Any reasonably well-informed business can now take advantage of outsourced Analytics Platforms and extract the maximum value from their data. This sort of “democratization” of Business Analytics is visible not only within business, but across businesses.

According to IDC, more than 90 percent of Internet of Things (IoT) data will be available on Cloud Platforms in the next years as Cloud Computing simplifies IoT Data Management. Big Data Analytics: For the Governments of the Future points out that although government sectors have been somewhat slow in adopting Big Data Analytics when compared to industry, the government agencies now realize that Advanced Analytics can help them achieve higher governance through citizen participation.

Big Data Trends reveals that IDC has already predicted that the Big Data market, along with the Business Analytics market, is slated to grow from $130.1 billion in 2018 to over $203 billion in 2020. A fundamental “cultural shift” toward “data-driven decision making” is the primary reason behind this sudden rise in Big Data Analytics.

Modern Data Analysts must be technically prepared to face the challenges put forth by data variety such as unstructured, semi-structured, and hybrid data emanating from the newer data channels. The Future of Analytics: What is All the Hype About? suggests ML and AI’s contribution to Analytics is more important than ever.

Sentiment Analytics is one area that may not overshadow other types of Analytics, especially in a marketing world blessed with print media and TV channels. The online consumers may be increasingly assessed by their expressed sentiments in social media or chat sessions, but a huge amount of Consumer Analytics is still conducted on the “non-live” world. It will be interesting to see if consumers express their buying sentiments differently on different media.

Gartner Indicates Self-Service Analytics is Here to Stay

Gartner’s Self-Service Analytics and BI Users Will Produce More Analysis Than Data Scientists Will by 2019 offers these key insights:

  • Business users with Self-Service Analytics skills will outpace the Analytics output of data professionals by
  • The Self-Service Analytics platform will survive solely on the merits of its governance model with its ability to support free-form data explorations.
  • The Data Science teams must support capable business users with timely guidance on using the self-service tools.

Convergence of Cloud Computing, Big Data, and IoT

According to the leading industry watcher Gartner, Self-Service Analytics and BI users will jointly output more Analytics than the Data Scientists by 2019. Self-Service Analytics and AI-enabled BI platforms are two pronounced trends in current business scenarios; “democratization of Analytics” is becoming a reality across the global business-sphere. As newer and better digitized businesses join the latest industrial revolution, the promising technologies like Cloud, IoT, Machine Learning, and Big Data will surely revolutionize the way businesses are conducted in the next five years. Soon we will not need technical experts to deliver informed business decisions.

Convergence of Big Data, IoT, and Cloud Computing for Better Future indicates that though Big Data, IoT, and Cloud Computing began as clearly distinct technology areas, Big Data has propelled the high adoption of IoT and Cloud in businesses because both of these technologies contribute significantly to Big Data Analytics.

According to Forbes, about half of enterprises are willing to adopt a “Cloud-First” strategy just to get the most value from Big Data Analytics. According to Forrester, about 50 percent of enterprises will adopt a “Public-Cloud-First” policy in 2018 as businesses look for a perfect balance between cost, control, and flexibility in their Analytics platforms.

Point and Click Analytics will Still Rule the Enterprise

Industry watchers like Forbes have provided widely researched data to establish that only a quarter of enterprises will use “conversational interfaces” with point-and-click Analytics systems, indicating the traditional query languages are still very much in use. Although natural language tolls like LNG and NLP are becoming de facto standards in most Analytics Platforms, not every user is ready for natural language.

The point-and-click platforms have another pronounced feature: readymade ML algorithms to tackle most Analytics tasks. As enterprises learn to depend on real-time Analytics Solutions, the AI platforms will be right there making recommendations and instant offers to customers.

The third feature of point-and-click Smart Analytics platforms will be the easy “meshing” of structured and unstructured data; and with Deep Learning emerging as a Data Analysis wizard, the business users can forget the old, troublesome days of text Analytics.

Insight Centers May be the New Norm in Organizations

Almost two-thirds of business organizations will deploy insight centers to cater to increasing customer demands for information. This trend will surpass both centralized and distributed information channels in traditional business models.

The Insights-as-a-Service (IaaS) market is likely to double as more and more businesses are relying on outsourced BI and Analytics capabilities. This trend will greatly aid the medium and smaller businesses as they do not have the necessary funds and resources to set up sophisticated Analytics centers. Forrester has predicted that almost 80 percent of companies will depend on outsourced insights in 2018.

Academic Campuses will Join Hands with Industries to Deliver Analytics Service

Many leading magazines and trade publications have noted that academic campuses are fast becoming trusted partners for enterprises in AI-enabled Analytics activities. This trend will continue as the universities or academic research centers with renowned Data Science manpower prove their mettle in solving complex business problems with advanced Analytics technologies.

The Rise of the Intelligent Enterprise

The article from Microstrategy titled 8 Analytics Trends to Watch in 2018 for the Intelligent Enterprise clearly states that the future of business analytics beckons the “Intelligent Enterprise,” which must be ever prepared to seize every regulatory or technological opportunity for business growth and profit. As businesses increasingly adopt a Cloud-First strategy, and many invest resources to develop Insight Centers of excellence, the IaaS market will boom and fulfill the Analytics needs of more businesses than was possible just a couple of years ago.

An offshoot trend of this “Intelligent Enterprise” will be a focused talent acquisition in Data Science stream. The Business Higher Education Forum (BHEF) and PwC jointly published Investing in America’s Data Science and Analytics Talent: The Case for Action, which indicates that the Data Science and Analytics-related job postings in 2020 will rise to a staggering 2.7 million.

Mingling of Real-Time and Batch Analytics

No matter how much tech-savvy business users wish to conduct Analytics through natural language interactions and real-time computing, they will always need to compare their real-time data with historical data. Take the example of a retail store that uses IoT data to monitor traffic: the analysts here will require historical sales data along with real-time traffic data to recommend the best distribution of sales staff. This meshing of real-time and historical data enables the delivery of actionable insights

The blog post 10 Trends that would Shape the Future of Data Analytics confirms that the IoT market is likely to grow from $170.57 billion to $561.04 billion within a five year period between 2017 and 2022. The connected data devices will also give rise to connected data, which means the machine intelligence of AI platforms will play a key role in establishing relationships between live and dead data and deliver more accurate results.

A case for Streaming Analytics is found in Streaming Analytics: Predictions of the Future, which points out Netflix has built its fortune on real-time analytics by tracking the “where, when, how, and what” related to the watching habits of its 75-million worldwide customers.

Streaming Analytics is also helping the auto insurance industry come up with more personalized insurance policies based on individual driving data – driving habits and risk analysis of past driving records.

Both these examples prove that the businesses of today want more than simple data collection and processing. The Data Analysis teams want to capture and monitor consumer data in real time, so that they can immediately respond via alerts, notifications, and recommendations to enable a truly personalized consumer experience.

The Double-Edged Sword: AI and ML-Enabled Predictive Analytics

Today, more and more global businesses are embracing AI-enabled Analytics Platforms. With recent developments like hosted Analytics Platforms, streaming devices, Big Data as mainstream, and data storage becoming cheap, and packaged algorithms, most businesses can now conduct sophisticated Analytics tasks. With AI-enabled Analytics Platforms increasingly becoming automated and tuned with ML and DL capabilities, the future generations of analysts will not have to be super techies with advanced Data Science degrees and years of technical experience.

Average business users will find modern Analytics and BI Platforms easy to use. With major Analytics tasks like data cleaning, Data Modeling, and data pre-processing becoming automated, the Citizen Data Scientists will be able to apply their domain knowledge to available data in highly capable Analytics systems. In the future, the business analysts may just have to learn to ask the right queries to get accurate answers from their smart systems. For more on this, see The Future of Machine Learning-Enabled Analytics.

Future of Analytics: Some Statistics

Here are some cool statistics on the future of Analytics:

  • This year, Deep Neural Networks (DNN) will be a standard component in 80 percent of Data Science toolboxes.
  • Next year (2019), Citizen Data Scientists will output more Analytics than actual Data Scientists. By 2020, over 40 percent of Data Science activities will be automated, encouraging more Citizen Data Scientists to participate.
  • Next year (2019), natural-language generation (NLG) will become a standard feature on 90 percent of Analytics and BI solutions. Also, 50 percent of Analytics queries will come from search, NL query or voice, or from automated sources.

The Future of Analytics

According to Assistant Professor of Math and Analytics at Florida Polytechnic University, Dr. Athanasios Gentimis, the future of Analytics lies in successful team building. The future Data Scientists must be willing to collaborate with subject experts to best utilize the Machine Learning or Deep Learning techniques in Business Analytics projects. The data experts are becoming increasingly more sophisticated and better informed about the data they capture, but finally it is the SME who must communicate the wisdom trapped in the data through a convincing story.

 

Photo Credit: kentoh/Shutterstock.com

About the author

Paramita Ghosh has over two and a half decades of business writing experience, much of which has been writing for technology and business domains. She has written extensively for a broad range of industries, including but not limited to data management and data technologies. Paramita has also contributed to blended learning projects. She received her M.A. degree in English Literature in 1984 from Jadavpur University in India, and embarked on her career in the United States in 1989 after completing professional coursework. Having ghostwritten and authored hundreds of articles, blog posts, white papers, case studies, marketing content, and learning modules, Paramita has included authorship of one or two books on the business of business writing as part of her post-retirement projects. She thinks her professional strength is “lifelong learning.”

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