Data democratization is to equip people with easy access to data/information without extensive or expensive training. The goal of data democratization is to allow non-specialists to be able to gather and analyse data without requiring outside help and is often referred to as citizen access. Studies have shown that data-centric organizations make better strategic decisions, have higher efficiency, improved customer satisfaction, and generate more profits.
Data Democratization as named by Gartner as one of the “Top 10 Strategic Technology Trends for 2020” is to equip people with easy access to data/information without extensive or expensive training. The goal of data democratization is to allow non-specialists to be able to gather and analyze data without requiring outside help and is often referred to as citizen access. Studies have shown that data-centric organizations make better strategic decisions, have higher efficiency, improved customer satisfaction, and generate more profits. In fact, Forrester predicts that such organizations are on track to make US$1.8 trillion annually by 2021.
Even when an enterprise wants to embrace democratization there can be difficulties in making data available freely. Data may be stored in silos, making it difficult for employees in different departments to access data. It requires three key factors of technological enablement — Data Access, Machine Learning, and Deployment.
Data Access - It is a view of all your structured, unstructured, and cloud data to allow easy access to all your information. By doing this you can achieve faster insights with minimum cost. Gartner predicts that enterprises will spend 50% of their time and cost just accessing various silos and types of data.
Machine Learning - Humans actively analyze the data to find out what’s expected and what's not, the system analyses the data and automatically asks additional questions to find unexpected information, which in turn causes humans to dig deeper. For implementing machine learning processes it is important to understand your users, how they will access this information, and importantly how you will keep it secure.
Deployment - According to Gartner, more than 85% of machine learning projects fail because they are unable to achieve any significant value to the business. The final step of democratization is having the ability to quickly deploy insights as required.
For data democratization to succeed, it needs to be trustworthy. It means keeping data safe and secure. The public internet is insecure, but a digital ecosystem that connects its users using private connections can improve performance, scalability, and resilience while ensuring secure data exchange.
While Data Democratization can fuel innovation, it can pose a serious threat to data security if not deployed properly. The way that data is collected, handled, and analyzed has become a raging debate across consumers, digital enterprises, regulators, and government agencies alike. Just as data governance and security are essential to data privacy, if done right, they can act as a great stepping stone for data democratization. Data democratization and the positive culture it can create is therefore critical to the long-term success of any organization.
Businesses that wish to benefit from data democratization will have to create it intentionally. This means an organizational investment must be made in terms of budget, software, and training. In the world of data democratization, breaking down information silos is the first step toward user empowerment. This cannot be done without customizable analytics tools capable of desegregating and connecting previously siloed data, making it manageable from a single place.
Bimodal strategies should be considered in the overall data democratization strategy. The bimodal strategy is the practice of managing two separate but coherent styles: one focused on data predictability and the other on data exploration.
Regulations and Data laws are increasing around the globe and users are more aware than ever of the potential harms from the improper handling of data. Because of this, maintaining proper data privacy is an imminent requirement for a digital business. There can be no data privacy without data governance. Moreover, there can be no data governance without data security. These are all stepping stones and not roadblocks for responsible and safe data democratization.
This golden era of data democratization requires trust. A recent study found that 81% of executives rate data as very critical to their businesses’ outcomes, and 76% of CFOs agree that having a single version of the truth is essential. Organization size does impact who owns the content and how you execute—but all businesses, regardless of size, should invest in a data governance strategy. This way, as smaller organizations scale, they have a strong data strategy that grows with them. Traditionally, data has been managed and owned by personnel working in IT.
While the ownership of data may remain unchanged, successful data democratization requires universal accessibility throughout the organization. The company’s Business Intelligence team can play a crucial role in fostering data accessibility, coordinating with IT to create and deploy policies that take data out of silos and put it into the hands of users. You want everyone to be able to access the data they need, but you also don’t want them to do the analysis that leads to flawed business decisions. To achieve this, consider implementing a data governance plan that weeds out inefficiencies and boosts accountability within and between business teams.
The organizations that are on the path of data-driven culture would need to decentralize the data systems, with respective roles, and teams holding the ownership on sharing of the data. This will in turn lead to the democratization of data with appropriate policies, procedures, and security applied. The policies should define the business goal, the goal targets, and how to evolve the practices. Based on this, advanced technologies need to be adopted, practiced, and implemented. Proper training should be imparted for the employees on these new technologies as well.
Banking and Finance holds a massive amount of data which is built over time. Data is crucial in this sector owing to huge customer interactions and compliance requirements. The data in finance is critical not only from a utilization point of view but also from a security point of view. Making the right data available to the right person is a critical function as per the Data Democratization process. On the flip side, ensuring the required security for sensitive data is also equally important for Data Democratization. While data can help in credit scoring, loan risk management, discovering customer portfolios, it also helps in fraud detection, AML/BSA checks, financial reporting, record keeping, and IoT enabled security checks. The role of data democracy, therefore, becomes even more significant in Finance.
There is a huge amount of data generated and logged in different medical centers. One major flaw when it comes to Electronic Health Records and other health data is that going through all the different portals and gatekeepers can feel overwhelmed. As the growth and spread of data have generated more information than one could analyze, breakthroughs in artificial intelligence (AI) have helped overcome this challenge. Medical experts are developing algorithms to analyze huge quantities of data and extract insights. These algorithms get more efficient with an increase of data, which could improve predictive analysis, enable greater personalization and easy access to enhanced care.
The democratization of data not only helps in enhancing customer experience at a broader level but also acts as a catalyst in the sub-functions of retail as a sector. By making the right data available to the right team, data democracy can play a critical role in adept decision-making based on facts and insights. It can help in market segmentation, customer profiling for having better-personalized promotions, loyalty management, and improved sales strategy. IoT and insights- driven Data Democratization can also improvise supply chain management and operational back-office efficiency.
Data Democratization is changing the way data is consumed, analyzed, and applied in an organization. It makes data available to more people which they can analyze and get insights with the help of software applications. Organizations that are ready to embark on the journey of Data Democratization needs the support of the people, technology, and processes guided by the right strategies and implementation plans. The challenges with Data Democratization lie in sharing the data without breaking the legal and privacy policies of an individual or organization. The way to go about it is to draw the front lines, agree all the way from individuals through the Governments adhering to the well laid broad policies.