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Top 10 Data Science Trends in 2020

Back in 2013, when it has been extensively reported in the media that Microsoft researcher David Rothschild has accurately forecasted this year’s 20 Oscar winners out of 24 with the help of his predictive model, our paths have once again crossed with big data fascinatingly. But then, it’s a glimpse into a smorgasbord of transformational capabilities that it brings.

For businesses, the lingering buzz around big data doesn’t ring hollow as they are unlocking unprecedented value by applying it. An Accenture survey has captured this pervasive sentiment; 94% of companies have admitted that their experience with big data has proved to be an exact match for their expectations. As such, related investments are coming in thick and fast. Capgemini Consulting predicts that three years hence organizational spending on big data technologies will end up at $114 billion.

To succeed in Data Science, it is important to maintain the chain of the right source of data to analysis to insight to action.

We have worked with many clients across industries for different data science consulting services either to build or just help them grow their data science competence. Through these experiences, we have learned and applied many best practices – below are our top 10 best practices to succeed in data science. Also, we will look at some of the success stories, and the hurdles that have got businesses in a sweat.

Cloud is becoming the new standard destination for data

The mass exodus of companies from a physical set-up to a cloud-based one has been a significant event last year. This has happened primarily because a host of data processing technologies are now fully operational on cloud platforms. Sensing the opportunity, leading vendors have spared no effort to drive companies towards the cloud. Offerings such as Salesforce’s Wave, IBM’s Bluemix, or Amazon’s Redshift have gained a healthy stream of customers. A hybrid architecture that combines the cloud and on-premises will form the backbone of data crunching shortly.

Hadoop in the spotlight

The open-source and distributed big data solution Hadoop has emerged as the default analytic framework for a large number of enterprises. In a sense, it is the data OS for them. Moreover, products that promise SQL-on-Hadoop are making life easier for data science consulting firms. These are light on company coffers as running them do not require the services of experts with specialized knowledge.

Increased demand for predictive analytics

Storing data in Hadoop is not the end in itself. It’s just the beginning, as analysis of the data stacked there is what generates valuable insights. An array of advanced processing engines in 2014, whether open-source Spark or other commercial alternatives that are designed to manage data with multiple attributes have enabled companies to get real benefits.

Options galore for learning

With more and more companies deciding to dabble in big data, demand for skilled data science consultants and data science consulting companies has soared. This has encouraged education providers to come up with various industry-ready training programs on the subject.

Data lakes cutting a wide swath

Data lakes are repositories that can store data in native formats for parsing at a later time. Since the concept is an effective tool in combating data fidelity and integration issues, companies are finding it useful.

Big data has hit the big time

Big data has a lot of use cases in an enterprise environment. According to the Accenture survey, 85% of users think that it has phenomenal power to change the workings of a business. They have identified customer relationships, product development, and management of business operations as the areas where the impact of big data will be felt the most. Almost replicating this tone, a QuinStreet enterprise report says, to 72% of companies the biggest advantage of big data lies in fast and spot-on decision making.

Here are a few instances of successful implementation of big data:

  • Fashion retailer Macy’s relies on big data to adjust prices of more than 70 million items in real-time.
  • Pharmaceutical company Merck is leveraging big data to streamline vaccine production.
  • Financial services giant American Express is mining big data for loyalty prediction.
  • Through the self-developed semantic search engine Polaris, Wal-Mart is pushing up its online conversions.

That big data is a boon to enterprises is no secret. However, the process of adapting to a big data-driven culture is not free from challenges.

  • The scope of big data is not restricted to a particular department rather, it’s a company-wide function that demands effective integration of various data fountainheads. So, firms that follow a siloed architecture are losing out in the game.
  • Lack of right talent is a big concern. From the implementation stage to the extraction and interpretation of insights, every part of the big data process must be handled by people with domain expertise. Ineptitude can make plans go wide off the mark.
  • As the number of security breaches has spiked in recent times, the protection of data sanctity is a top priority for companies.
  • Trying to manage big data through legacy systems is hurting companies. They must opt for the latest set of tools.

Use an Agile approach

Implement an Agile Data Science approach to ensure consistent progress. This means at a minimum breaking your project into 2 – 3 week sprints, with sprint reviews at the end of each sprint cycle to present demos of results achieved, as well as Agile task planning. Invite all stakeholders to the sprint reviews and sprint planning, to reduce uncertainty, manage risks, gain a common understanding, and contain the scope of the project.

Go beyond insights and predictions, to optimized actions

Traditional business intelligence (BI) tools provide insights into the state of the business, but they will not help you understand what might happen in the future and that is where you require predictive analytics. Predictive analytics can help you understand what can happen in the future, but it will not help you get recommendations on what to do about that — for that, you require prescriptive analytics for decision optimization. It is the only technology capable of considering multiple trade-offs between and can recommend the next best action or plan into the future

Demand for Data science Security Professionals

The use of artificial intelligence (AI) and machine learning (ML) is giving rise to many new roles in the industry. One role that is and will be highly in demand is of data science security professionals. As both AI/ML entirely depends on data, and to process this data efficiently, data scientists must have expertise in data security. Though the business market already has access to many experts who are proficient in data science and security, there is still a need for more data security professionals who can process data to customers securely.

Natural Language Processing (NLP)

Natural Language Processing (NLP) provides a strong value proposition by giving business users a simpler way to ask questions to receive insights. Conversational analytics advances the concept of NLP a big step forward by enabling such questions to be posed and answered verbally rather than through text. In 2020, NLP and conversational analytics will continue to boost analytics and BI adoption by significant numbers, including new classes of business users, particularly front-office workers.

A big driver of this trend is that NLP has made its way firmly into data science after giant breakthroughs in deep learning research and has fueled the full-on integration of NLP into routine data analytics. Check out this informative guide for a technical overview of the most important advancements in NLP over the past few years

Practices that move things in the right way. The best anodyne to fight the pain generating from the issues discussed above is to develop a dedicated big data strategy. To conclude, a ‘separate the wheat from the chaff’ approach is needed to discover the truth in big data i.e., enterprises must clearly understand what information to keep and what to discard.

  • Kick-start the initiative by building a stable governance framework that will act as the mainstay. This should include carving out a top-level leadership role for data, establishing benchmarks for measurement, and picking up the use cases
  • Next, reflect on the combination of technologies that will exactly serve your purpose because there is no one-size-fits-all.
  • Create a big data-ready talent pool. Do conduct skill development programs at regular intervals and don’t hesitate to harness external resources when needed. You may also consider forming tie-ups with academic bodies to hire prospective recruits.
  • Lastly, but in no way least important, make sure the big data platform you choose must be agile so that you can incorporate the latest changes quickly.

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