What Could Derail Your Data Science Initiatives

 In Artificial Intelligence, CEO Corner, CFO Office, Digital Transformation, From the CEO

Across industries, data science is helping organizations extract meaningful data using specialized methods, make more informed business decisions, and identify trends. Although most companies look to benefit from various data science opportunities to raise productivity, improve decision making, and gain competitive advantage, the path to data science success is lined with hurdles: you have to set a strategy, draw a detailed roadmap, invest in new technology, reinvent processes, and bring about a transformation in your organizational culture. Here’s what could derail your data science initiatives:

'Terminator' of Artificial Intelligence

    1. Legacy systems:

      Introducing data science to a legacy ecosystem brings about its own set of challenges. Since data science demands increased computing power to rapidly gather, store, and analyze massive volumes of data, legacy systems are incapable of such large volumes of data from different sources. What is required is integration between your old and new systems so that there can be a seamless flow of information. In the manufacturing sector, for instance, organizations need to create a unified data model and integrate data across all independent systems in the manufacturing process starting with design, production and distribution in order to improve business efficiency minimize wasted materials and activities.

    1. Lack of right skills:

      Another challenge in successfully implementing a data science program is the lack of right skills. Most companies falter at data science because of the talent gap – while many organizations do not have the resources with skills necessary to interpret big data, there is also the lack of suitable candidates in the labor market. According to PwC, only 44% companies have a sufficient pipeline of talent to undertake the deep analysis of data. However, companies often overlook their existing talent; individuals in marketing analysis and data management can serve as a great starting point to translate data into insight. Not all talent has to be freshly recruited – training existing staff can work just as well during the initial phase.

    1. The fear of data privacy:

      Data privacy and security rank high on the list of data science challenges for companies. Since most analysis is centered towards customers, companies often worry about securing data to protect customer privacy. In the retail industry, for instance, customer behavior data, as well as financial data, is always at risk. In order to avoid unauthorized access to data, there is a need to implement elaborate processes that protect and safeguard data at all times. As key business decisions are mostly data-driven, you need to improve the quality and integrity of your data to achieve the intended outcome from your data science program.

    1. Lack of integration between business and IT:

      Organizations with centralized IT teams often act as a bottleneck to data science success. At the same time, business teams that make technology decisions without involving the IT team cause problems to arise that are complex, costly and time-consuming. Although autonomy helps both business and IT operate independent of each other, a certain amount of balance and coordination is required to stay in the loop. By creating centers of excellence where business and IT come together, organizations can have a team who understands the business and can translate problems into solutions.

    1. Not knowing what data to use:

      With such humongous volume of data being generated from millions of systems in every organization, deciding which data to use becomes a major challenge. Organizations often do not know where to apply their data science program. Moreover, when combining data across systems, inconsistent data formats also pose a major hindrance. Build a complete picture of your operational activities to discover which data to be used for analysis in order to achieve the right insights into business operations, customer needs, and market trends.

    1. No compelling business case:

      Since data science programs are hard to quantify, many companies looking to benefit from it find no compelling business case. Strangely, 50% of companies feel their business processes are not mature enough for data science. What’s even more strange is that companies that have already implemented a data science program also report high rates of inadequate know-how. However, irrespective of a business case, with the right data science initiatives in place, you can be sure to benefit in the form of productivity gain, increased revenue, reduced costs and improved competitive standing.

Overcome Challenges

With data quickly becoming every company’s most valuable asset, sharing and integrating it across applications and departments is vital. Although it is widely accepted that data science is the key to success in today’s dynamic business world, companies often struggle with implementing their data science programs. Legacy systems, lack of right data science skills, the fear of data privacy, lack of integration between business and IT, not knowing what data to use and the absence of compelling data science business cases serve as major hurdles to data science success. With technology innovation progressing rapidly, organizations need to reinvent their culture in order to overcome challenges and achieve optimum data science results.

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