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It is only when Data Analytics start meshing with advanced technologies like Machine Learning and Deep Learning that enterprises will reap the benefits of their analytics and Business Intelligence (BI) activities. Those technologies require well-planned Data Science principles built around business need. Such Data Science use cases are now mature enough that business owners, leaders, and managers are instinctively able to repeat the proven models without worrying about the value of outcome.
A sound Data Science platform promises value-added outcomes of business data. The most valuable contribution of this amazing science has recently been perceived in the “360 degrees view of the customer,” which enables modern businesses to go beyond the surface interactions with the customer and dig deep into their personal demographics, preferences, tastes, and opinions. DATAVERSITY®’s article titled Unlocking Power First Party Data aptly describes how quality reigns over quantity in case of customer data.
Enterprises are always looking to build, find, select, and deploy Advanced Analytics solutions that can easily scale with changing business requirements for maximized ROIs. It is common knowledge that many Data Management solutions are not possible without investing in the growth and advancement of Data Science discipline first.
As more and more business operators are demanding more control over their internal data processes and activities, the emergence and growth of a parallel science of Self-Service Data Analytics is the only answer. According to Charles Zedlewski, senior vice president of Products at Cloudera, “it is critical that larger organizations identify an enterprise open source vendor to both provide support and guidance as they implement Open Data Science.”
How Machine Learning is Transforming Data Science Use Cases
The science of Machine Learning (ML) suddenly resurfaced around 2012, when the four technology giants, Microsoft, Google, Amazon, and Facebook began to explore ML-based applications to enhance their business interests. Currently, path-breaking advancements in Machine Learning are visible all around us. Further, Big Data applications have helped ML to enter the golden age of Data Analytics, and if market reports and forecasts are to be believed, then Data Science will rapidly become cheaper and more available, in terms of Open Source, hosted BI platforms and ready-made algorithms.
The Artificial Intelligence and Machine Learning powered Data Analytics platforms of the future have been visualized as democratic platforms empowering the grassroots and elite business users alike. This trend will also result in the rise of the Citizen Data Scientist in every organization. . As an example of that effort, one can think of the Cloudera Data Science Workbench.
The Underlying Power of Data Science: Technologies and Tools
The most convincing argument in favor of implementing Data Science use cases is that no matter how much data or Big Data pours into the enterprise through various data pipelines, they will remain unused or underused unless the matching tools are available to extract “knowledge” from them. As in-house Data Science setups are very expensive and talented Data Scientists are beyond the reach of medium- or small-sized businesses, organizations have to replay more on Open Source Data Science platforms to meet their Data Management needs. Anaconda, an Open Data Science platform, can be an answer to enterprises looking for economical solutions for enterprise success of Data Science.
As solutions like this one has the capability to offer differing levels of technical empowerment to anyone from a common business user to a seasoned Data Science team. The significant feature of this solution is that it can scale with the limited or expansive needs of its users. The ideal solution is to combine the in-house talent with the extended support of the open-source vendor to achieve the actual results. You may also review a DATAVERSITY® webinar on the technical challenges of Self-Service Data Analysis.
Data Science Use Cases Promise New Business Opportunities
Beginning with a handful of raw-data analysis, today Data Science has evolved into a mature field of advanced data cleansing tools, smart algorithms, self-driven systems, and exotic visualization platforms. With this much growth and advancement, businesses are now realizing that data-centric business benefits were hardly available to enterprises say even a decade ago.
It is all about data opportunities now, and the businesses that make the most economical but effective investments in data technologies will lead in their chosen industry sectors, far ahead of their competition. With mobile and Big Data applications, petabytes of data are easily available to all types of businesses, this is the perfect time to devote attention to the advancement of Data Science to make good use of the data. Readers of this post are encouraged to review The Six Steps for Data-Science Driven Businesses to leverage the hidden insights in their business data for increased productivity, lower costs, and higher customer retention. Also review DATAVERSITY®’s article titled Where Is Data Science Hype Cycle, which shows the other side of the truth.
Data Science Helps Capture Data Volume, Variety, and Value
The article title Why and How Data Science Matters to Businesses explains why Data Scientists are the most valuable assets to any organization. Right now, even with vast volumes of data and advanced tools, the average business user cannot extract the necessary intelligence and make quick and effective decisions on his own. Till truly Self-Service Data Analytics and BI platforms are available to the common business user, these “super-heroes” will continue to provide value-added services to their organizations by enabling data-driven, actionable intelligence.
Though Forbes & Adrian Bridgewater claim that Data Science Isn’t Just for Data Scientists, it is anybody’s guess how far IDOD can empower ordinary business users with ready-to-use data models. One can only hope that with the advancement of data technologies and tools, one day in future, the non-IT business staff will be able to use prepared data models and algorithms for their everyday use. The article titled Striking Oil with Data Science: The Century’s Hottest Career suggests that the academic discipline of Data Science compels students to think in a structured fashion. Now, and with increased certification programs in Data Science, and practical project exposure, ordinary data users have a fair chance of transforming into capable Data Scientists.
Reinventing Organizations with Data Science Use Cases
Modern businesses reply on real-time insights and on-the-spot decision making. This can only happen with technologies like Big Data, IoT, mobile analytics, sensor-guided data piles, and related tools. The article titles The Most Practical Big Data Use Cases of 2016 explains how large businesses like Wal-Mart or Rolls Royce are using Real-Time Data Analytics to improve their R &D, manufacturing, and sales processes.
As streaming data as those from fitted sensors can help the business decision makers view the problem, the analysis, and the probable solution all at the same time, these decision makers can come up with highly agile decisions, which would hitherto take days or months to make. This article also talks about the contribution of Big Data in healthcare that has vastly improved the state of emergency patient care.
In the given context, you may find the infographic titled How the Top 10 Industries Use Big Data Applications interesting, which seeks to build a cost-benefit analysis of Big Data implementations in enterprises. In 10 Industries Using Data Science and How, you can get acquainted with the practical applications of data-driven technologies and tools in insurance, financial services, telecom, tourism, eCommerce, and other industries. The DATAVERSITY® source titled Data Science to Boost Global Prepaid Market to USD 70 Billion makes an interesting case for the data-driven telecommunications market.
The Gartner 2017 Magic Quadrant: Data Science Gainers and Losers gives a good look at the relative merits and demerits of some 16-odd Data Science use cases and solutions as well. Certainly, the way that Data Science has already transformed the Data Management industry is phenomenal, yet the changes have really only just begun. The number of use cases will only continue to grow far into the future.