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Build a Data-Driven Enterprise to Predict & Optimize Business Outcome Through Digital Transformation in Business

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    November 12, 2019
Build a Data-Driven Enterprise to Predict & Optimize Business Outcome Through Digital Transformation in Business

Key observations

  • Enterprises’ investment is high in Data and Analytics
  • Only a few could interpret and take insightful information from data for competitive business advantage
  • Many are still focusing on achieving data quality and governance

Building a data-driven enterprise

Businesses should make data and analytics role more active and dynamic. Data and it’s analytics help organizations to answer the bigger question, “How am I doing in business”. It is the basis of every decision, every process, and every interaction in the business and also maximizes the chances of an optimum business outcome. To gain additional business value, technology heads should take advantage of data science and its practices. Businesses can not overlook the rise of data science technologies such as machine learning, artificial intelligence, Natural Language Processing to model structured and unstructured data to get insight for decision making and gaining new business value.

Priorities for enterprises

Looking at the current business scenario and depending upon the applicability, operational risk, and business value, CIOs should focus on below mentioned strategic priorities of Data and Analytic. You can take help of professional digital transformation consultants.

Quality of the data

Every business is different and the landscape is changing rapidly. Many organization moving towards digitalization - digital transformation in business. Enterprises are adopting digital capabilities, accessing the larger and diverse dataset. However, how can CXOs quantify the impact on their businesses?

Data plays a vital role in gaining insight into the business. The good quality data generation is still considered as a technical challenge rather than a business one. Eventually, business decisions and quantification of business success change from “gut-feel” to “fact-based”. Therefore having the quality data is of utmost importance to drive meaningful insight for best and quick data-driven decision making.

Any strategic business initiatives now require sufficient data for competitiveness, growth, and agility in the business. Data is driving the future of the business therefore it is also not at all acceptable to have flaws in data. Businesses must focus on having quality and flawless data.

All functions of the organization generate data and there has to be a unified and well-known way of recording business-related data. A standard process to generate data helps you to get a good quality of data.

Bottom line “You can’t manage what you don’t measure.” Therefore it is required to measure the quality of the data in any objective and quantitative way to ensure that the efforts are making the difference in the enterprise and reaping the significant benefits as a result.

Management of the master data

CXOs must define the clear line of sight to business benefits. A detailed introspection is required to know the most important master data of the enterprise that lays down the business foundation for everyone. Brainstorming and noting down master fields is not enough to help transform business. There are lots of benefits of digital transformation in business. But seeking business transformation without any approach such as master data will lead to far higher costs of data integration and decrease the productivity by duplicating efforts to manage business data.

With the help of master data management, enterprises will achieve higher business engagement, better reporting, improved risk management, ability to develop measurable business-oriented key performance indicators, greater business agility, improved end-to-end process, better analysis and decision making.

Effective information governance strategy

A single data or information has many different uses, and there are multiple reasons why some information is more important than other information. An effective governance strategy is required and should be designed by the enterprise to decide when, why, where, what and how to use information. This strategy also helps an organization to decide about the information like what to govern, share, manage and where it requires change over time.

A context-based or adoptive based approach of governing data is the response to manage increasing diversity and complexity of business scenarios that also have a vital dependency on data and analytics. This approach helps enterprises to identify an issue whenever and wherever it comes, evaluate it and take remedial actions on it based on the business outcomes and value desired.

Empowering citizen data scientists

For greater business context and value, citizen data scientists effectively embrace data science in the enterprises. Citizen data scientists are capable enough to perform both simple and complex analytical tasks that earlier required experience. They are the one who bridges the gap between mainstream BI and advanced analytics techniques of specialist data scientists.

If we talk about business transformation tools, citizen data scientists do not have any coding skills but they develop models using visual drag-and-drop tools. Many data science experts lack business and industry vertical domain expertise compare to citizen scientists.

Food for thought

Data is prevailing everywhere in the business. To gain business productivity and efficiency, it’s time to focus on investing data science’s innovation approaches. An exclusive data and analytics strategy helps enterprises to set the pace, direction, and cadence of the business aligning with the business strategy. In enterprise website development, enterprises who use data and analytics strategy for their business-oriented approach are most likely to deliver positive and measurable business outcomes. At the same time, it is of utmost importance that the framework of creation, access, consumption, and valuation of data should follow the context-based standardized approach.

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