Digital transformation is rapidly changing the business world, including the life sciences industry. With an increasing level of networking and connectivity among people and machines/devices, exponential growth in new data is likewise occurring. Nevertheless, today’s businesses already own massive volumes of historical data, and in most enterprises, data, technologies, and people are siloed.
For instance, life sciences companies that have their healthcare provider (HCP) data distributed across disparate data sources find it challenging to obtain a complete HCP view about specialties, spending, and affiliations, to achieve better sales and account management. However, the challenge is not only about bringing all the data together from all sources to create a single source of truth, but about organizing data for continuous self-learning as well. In other words, today’s enterprises want to go beyond having only clean and reliable data; they want to learn everything about customers (i.e., about HCPs), products, and their relationships, to measure and improve their operations and provide better customer engagement.
Therefore, master data management (MDM) solutions that only provide clean and reliable data will not suffice because today’s modern enterprises are looking for better data management solutions to harness the value of data and obtain actionable insights from that data.
Under such circumstances, vendors that can provide an MDM platform that performs continuous data organization, is self-learning, recommends actions, and addresses the aforementioned challenges are expected to secure leadership positions in the market.