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Are Data Scientists Needed in the Self-Service Analytics World?

By   /  July 5, 2018  /  No Comments

Data Scientists and Self-Service Analytics Fully-automated or semi-automated software systems will begin deliver more reliable Analytics and Business Intelligence (BI) reports than human Data Scientists by 2019 says Gartner. As Artificial Intelligence-powered BI technology heads towards full self-service, a general concern in the Data Science community is whether human Data Scientists will become obsolete due to the presence of super-intelligent Analytics and BI tools.

Is Self-Service Business Intelligence a Myth?

Currently, many Analytics and Business Intelligence tasks are handled by semi-automated or fully-automated Analytics platforms, especially ones powered by Machine Learning (ML) tools. Data Mining was an area dominated by human Data Scientists until recently advanced ML-enabled tools took over many tasks.

In Gartner’s survey of over 3,000 Chief Information Officers from across the world, respondents unilaterally agreed that Self-Service BI has been embraced to democratize the use of advanced techniques in daily business. Data Mining techniques closely guarded by human experts for years have now suddenly been replaced by advanced ML tools. These tools can detect patterns in data, establish correlation, and extract the required insights as and when needed by ordinary business users.

Self-Service BI is no myth, as current businesses of all sizes are routinely using packaged Machine Learning algorithms for profitable decision-making. The Algorithm Economy is here to stay. There are two obvious advantages of using packed algorithms for business analytics: the cost and the instant availability.

Gartner has predicted that, “the analytics output of business users with self-service capabilities will surpass that of professional data scientists.”

Two marked trends are now visible in the Self-Service Business Intelligence world: the deep fascination with click-button analytics rather than coding analytics function, and the preoccupation with virtual data repositories.

The Role of Data Scientists in the Self-Serve World

While “data culture” is quickly spreading, Data Scientists are making value additions to the business by leveraging technology to deliver quicker and more accurate solutions to all types of users.

The Self-Service BI revolution brings Data Scientists to the business corridor where they discuss complex Analytics issues with other employees. 2018 will be the Year of Self Service Data Science for the Enterprise echoes the sentiments of the Gartner Report, stating that the tremendous growth of Citizen Data Scientists and Machine Learning tools will result in the rise of Self-Service Analytics and Self-Service BI. This year, businesses will probably witness teams of Citizen Data Scientists and trained Data Scientists working together to achieve profitable goals.

The DATAVERSITY® article Fundamentals of Self-Service Business Intelligence describes a real-life journey into the business practice of Self-Service BI. It points out that only a Data Scientist is qualified to bridge the gap between “raw intelligence” extracted from smart platforms and decision-friendly insights flashed through dashboards. The average business user may accomplish somewhat more than just filtering and grouping data in the self-serve world, but cannot achieve advanced visualization tasks.

Data Preparation and data extraction remain the important challenges in automated BI platforms, and the complex interrelationships between many related technologies like Hadoop, Big Data, Data Discovery, and others will pose a barrier to technology access, use, and comprehension in the self-service world. “Assisted BI” may be a better term to describe the future of Self-Service Business Intelligence. Moreover, Data Security and Data Governance will become serious issues in the Self-Service BI world, for which enterprises will have to engage experienced data professionals.

The Rise of the Citizen Data Scientists

Why Every Company Should Use Self-Service Business Intelligence Tools suggests that ordinary business users need self-service platforms to get their jobs done quickly and easily. The topmost reasons for this transformational business shift toward Self-Service BI are lack of skilled Data Scientists, and the impending talent gap in IT professions forecasted by McKinsey years ago.

Businesses will have to find solutions to this manpower gap, one of which is procuring, building, and deploying Self-Service Analytics and BI platforms to fill their in-house needs. Of course, merging technologies like Cloud, IoT, and Big Data also strengthen the “viability” of self-service platforms in the long run. In such a self-driven Analytics world, the Citizen Data Scientist can serve as a partner and collaborator for the trained Data Scientist.

How Can Data Scientist Add Value to a Self-Thinking BI Platform?

Self-Service Business Intelligence is Big, but is it for Everyone? states that the Self-Service Business Intelligence solutions are currently catering to two widely disparate consumer segments: the ordinary business users and the professional IT teams. While the business users are excited about becoming self-sufficient in routine analytics or BI tasks, the IT team members are also enthusiastic about extracting deep insights with the use of automated or semi-automated BI tools.

The article cites a 2017 survey about user adoption of Self-Service Analytics conducted by the market-research firm Impact Analytics. The survey respondents were IT professionals representing the central IT facilities in their respective companies. Impact Analytics revealed that almost two-thirds (over 65 percent) of respondents reported they had either provided or were about to provide Self-Service Analytics to their business users. The respondents reported that over half of the business users have direct access to self-service analytics platforms. The article implicitly suggests that Data Scientists are still an important part of the self-service world.

Predictive Analytics World questions whether human Data Scientists will vanish from the enterprise with the sudden rise of the Citizen Data Scientist. Its conclusion is that though advanced tools can bring business users close to a first-hand analytics or BI experience, the role of the Data Scientist can never be erased. “Data Dabblers,” as the author calls average business users, will never be able to gain the depth of knowledge of a real Data Scientist.

A UK-based industry watcher, IT Pro, discusses the current talent gap. It indicates that the European Commission has made a serious commitment to fill the talent gap in Data Science across Europe. Many notable companies like SAS Institute have launched post-graduate degree courses to fight the talent gap in Data Science for Business.

8 Jobs Every Company will be Hiring for by 2020 suggests that in two years, businesses will feel a tremendous void in Data Analysis talent who can bridge the gap between the average business users and the avalanche of data-centric insights and intelligence poured out by automated analytics or BI tools. According to the World Economic Forum, though the recent technological disruptions are threatening white-collar jobs across the world, Data Analysts will be in demand to aid the Self-Service Business Intelligence platforms.

Self-Service BI or Assisted BI: Which Is More Achievable in Businesses?

The Algorithm Economy is pushing business communities toward “insights” from plain information. However, the core activity that delivers business insights is analytics, and without advanced analytics or BI tools, businesses will head for failure in the future world of global competition. This is where Embedded Analytics come in, and The Ultimate Guide to Embedded Analytics: Keys to Product Selection and Implementation provides an overview of assisted BI. It states that in an Embedded Analytics project, from the initial step of defining a project to the final step of launching an application, requires specific analytics knowledge and skilled manpower. Assisted analytics will be needed along with self-service in the increasingly competitive business world.

Self-Service Analytics and the Illusion of Self-Sufficiency explains why Self-Service Analytics is so popular among businesses. It refers to the new platforms as a “double-edged sword.” While the ease and power of Self-Service BI is undeniable, the long-range maintainability of these platforms in terms of data security, Data Governance, and data spillage poses a big challenge. The implication is that highly skilled IT Teams will be required to maintain these systems.

Risks and Benefits of Self-Service BI

The biggest benefit of the Self-Service Analytics and BI platform is that it empowers ordinary business users to become Citizen Data Scientists. While performing their daily functions within strict time constraints, the business users will certainly find the self-serve platforms handy and accessible to get their jobs done without much fuss.

The biggest disadvantage or “risk” of a self-service platform is that users may fail to derive insights from available data, misinterpret the results, or misapply the insights. While the human Data Expert knows how to talk to the machine in case of problems, the average business user does not have such skills. In many situations the Citizen Data Scientist will be compelled to turn to the real Data Scientists for help and support.

The data explosion, increasing data types, emerging technologies, and the Cloud will increase the challenges of Self-Service Analytics, despite Data Preparation and data access tools. There are certainly important issues that need to be dealt with involving Data Security and Data Governance in Self-Service Analytics platforms, while there is a strong case for a “distributed BI framework” with full attention to security and governance issues.

 

Photo Credit: Panchenko Vladimir/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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