McKinsey Global Institute’s reports suggest that data-driven organizations are 23 times more likely to acquire customers, six times as likely to retain customers, and 19 times as likely to be profitable as a result.
Yet today, many companies neglect the potential of data and analytics. According to Gartner, fewer than 50% of documented corporate strategies mention data and analytics as key components for delivering enterprise value.
There are others, however, who recognize the benefits of data and analytics but fall short in effective implementation and adoption.
Data and analytics are key drivers of organizations’ digital transformation efforts. In the emerging digital economy, companies are compelled to generate insights that are forward-looking and progressive to stay in the competition. According to Forrester, insights-driven businesses set the pace for global growth. Insights-driven businesses are growing at an average of more than 30% each year and by 2020, are predicted to take $1.8 trillion annually from their less-informed peers.
Clearly, leading organizations will be using data and analytics to gain a competitive edge.
Data science technologies and methods are rewriting the dynamics of business and propelling organizations accelerating their digital transformation journey. IDC predicts that worldwide spending on digital transformation will reach $2.3 trillion in 2023. At the crux of digital transformation, however, lies good data, effective governance and an effective data strategy that defines how data is used across the enterprise.
Here are a few things you should keep in mind for fully leveraging data and paving the way for a successful digital transformation journey.
The tenets of data strategy should be structured to achieve agility in processes and consistently deliver value to the business. Agile strategies enable organizations to evolve and improve over time, adapt to changes and allow contribution from all levels of the organization.
Data strategy refers to the methods, services, architecture, usage patterns and procedures by which data can be acquired, integrated, stored, secured, managed, monitored, analyzed, consumed and operationalized. Organizations embarking on a digital transformation, need a plan to judiciously use data as a corporate asset and formulating a data strategy is the stepping stone to achieve it.
An effective data strategy ensures that all data initiatives are standardized and structure provides a template that is repeatable. Uniformity across initiatives ensures efficient communication throughout the enterprise to define and account for all solutions that make use of data in some manner. Data strategy provides a comprehensive checklist for setting up a roadmap that leads to a successful digital transformation, which is an active pursuit among organizations looking to modernize.
Organizations looking for digital transformation should ensure generating the utmost quality of data at a quick pace. This calls for cleansing and reviewing data critical to business and blending data from disparate systems into a coherent and standardized form. Organizations should catalog and secure data and make it available to authorized users across the enterprise to avoid mismanagement of data and counter activities that might hamper its quality.
By using a crowdsourced approach and decentralizing the ownership of data among appropriate stakeholders, organizations can automate data quality management and ensure the quality of data at high levels. This allows various departments in the organization to bring their expertise and at the same time create transparency and accountability into data quality management.
Information governance is an imperative aspect of a successful digital transformation journey. Governance, be it for a country, state or data, entails a universal model with a set of defined roles, guidelines, processes and policies.
Data Governance helps to manage data assets and ensure their integrity, accuracy, and security. It provides a structure for organizations to control data, without which data assets lose much of their strategic value.
Without effective information governance, organizations are left in a state of uncertainty about the roles and responsibilities, and usage of data in general.
Some of the key elements of information governance include:
The processes of generating insights from data involve several complex activities – from preparing clean, consistent and usable data to generating appropriate statistical models and using the right form of visualizations to convey the interpretations of data.
In addition, all these processes involved in preparing, interpreting and analyzing data for analytics, have been largely manual and require the intervention of specialized data scientists, who aren’t available in abundance. As the data volumes grow in size, the manual process of data analytics becomes inefficient due to the complexity that emerges with the ever-increasing size of data.
By incorporating AI, the process of generating insights will be transferred to and nested in technology. AI does all the manual heavy-lifting tasks thereby eliminating errors, biases and ineffective decisions. AI relieves the dependence on data scientists and manual processes to generate insights and hastens the entire value chain of data analytics.
Data and analytics are tools that organizations should leverage in today’s fiercely competitive business environment. The potential that lies in leveraging data has been long realized and organizations should put data at the centre of their digital transformation journey.
The core of digital transformation involves robust information governance, effective data strategies and assurance of quality in data. By laying their foundation in governance and data quality management, organizations can open a floodgate of opportunities to improve sales, customer service, formulate agile practices and generate valuable business insights using artificial intelligence.