Businesses are beginning to realize the value of their data and accept the concept that "every company is a data company.” Industry leading businesses are increasingly differentiated on how they use data as a core asset to get better leading indicators and drive better business strategies.
The problem is that while data is being generated daily at an incrementally high rate, much of it is sitting in siloed systems and is often difficult to access for analytical purposes. So, decisions are made from a partial perspective, dependent on what system is being used. The challenge is to consolidate data so that the right data can be accessed by the right person, at the right time.
One challenge presented by digital transformation is effectively using the massive volumes of data that it creates. It also introduces an unprecedented opportunity to bring together a vast amount of data for better decision-making and prediction.
But the problem CIOs often encounter is that enterprise data is stored in silos, making it nearly impossible for companies to get a comprehensive view of their data. Since data comes in different formats, structured and unstructured, including spreadsheets, documents, databases, log files, videos, images and text; the process of extracting and integrating the various fragmented sets of data is problematic.
Not to mention, all the diverse data sources have their own connectivity issues. In the past, the technology required to connect to and gather data from disparate sources was complex and often didn't provide an effective solution.
Growing volumes of data have also led to the emergence of modern technologies like data lakes, which enable enterprises to store and handle large volumes of structured and unstructured data in their native format. This, however, has resulted in further challenges as complex staging environments have to be established to move data in and out of the lake for other systems to operationalize
Many legacy systems did not anticipate the broader scope of how their data would be used. Integrating the data from these legacy systems to modern external platforms is difficult. In some cases, networks and firewalls were not designed to enable the movement of large amounts of data. IT security policies can be additional barriers to collecting and organizing large amounts of operational data.
For businesses to make better use of all their data, they will need to adopt a new, agile, data-centric architecture that can handle the modern rate of change.
The approach in the past was to create multiple data warehouses for reporting and analytics. The problem with that approach was that they were domain-specific and often required significant resources to extract, transform and load the data. When they were used, there were often requests to expand or modify the data warehouse to satisfy new users’ requirements. Any re-organization of the data warehouse required significant planning and technical effort.
In recent years, data lakes have been seen as providing faster access and a better structure for regular analytics. They provided an intermediate location between operational and analytical data storage, where very large volumes of data can reside in an unstructured or structured format.
Not only is the right data management tool needed to consolidate and transform the large volumes of disparate data formats, but there are organizational elements that need to be established to ensure the full benefit when corporate data silos are consolidated.
As organizations become increasingly data-driven, implementing a data governance framework is critical. It is not confined to complying with government regulations about data use and privacy.
Good data governance can foster a culture of data exploration and analysis and empower employees to confidently work with data. A data governance framework can inspire confidence in data and lead to a curiosity that will generate new insights and action.
Most organizations deal with an incredible amount of data. Collecting data isn’t the same as understanding it and businesses need to improve their employees' skills to improve data literacy. By making organizational changes to improve data literacy businesses can speed up data analysis and digital transformation initiatives across the enterprise. Bernard Marr, strategic business & technology advisor said, “the more empowered employees are to read, write, analyze and understand data, the more they will be able to take advantage of the benefits that data offers.”
Insurance companies have to collect huge amounts of data for performance management, risk mitigation and customer service. Many are saddled with multiple sources of data and the technical debt of legacy on-premise systems that manage different lines of business. While moving to the cloud may solve some of the problems and help insurers to be more agile, these can end up becoming data silos in a different guise. As for modern technology, the use of IoT (Internet of Things) data can give insurers greater visibility while significantly increasing data volumes. In addition, insurers have repositories of data that data science teams analyze.
These create a range of difficult-to-reconcile data sources. Immaturity in data governance often means that key terms may not be defined consistently, creating a bottleneck for consolidation.
Even Insurtechs have to deal with data issues arising from data in various systems.
The benefits of consolidating siloed data are clear for analytics and operational activities:
Trying to manage data transfer and consolidation involving data from different technologies, in batch or real-time streaming, with several data integration tools, makes it difficult for organizations to be agile and to quickly incorporate, integrate, analyze, and share their data.
The value that Synatic's Hybrid Integration Platform is boundless. Synatic’s HIP offers a single system that can identify, collect and contextualize disparate data across an enterprise enabling businesses to deliver data that is meaningful, ready to use and specific to a user’s needs. Centralized data also reduces the overall number of systems that must be managed and protected from potential threat vectors.
Synatic has MongoDB's data lake technology built into the platform, which allows businesses to store data in the appropriate storage solution for their use case. The data transformation capabilities of Synatic enables it to perform the Extract-Load-Transform (ETL) processes for loading data in data lakes. Whether that be within Synatic’s MongoDB data lake, or within a 3rd party data lake.
Critically, Synatic’s ability to store data has allowed many customers to circumvent the need to stage their data, and further allows seamless movement of the data out of the lake into other systems. This means that the lake isn’t purely the end point of data, but the data is operationalized to move both into and out of the lake.
In order to move to a world where data is kept, consolidated and then used, new technologies and approaches are required. IT departments should aim to reduce the amount of time they spend storing, analyzing and presenting information to users, by establishing practices and implementing a data automation solution that promotes data access and analysis.
For insurance companies, moving to the cloud is needed to enable the type of computing power that can leverage the huge data sets. By consolidating all their data using a Hybrid Integration Platform, insurers can become platform players - orchestrating the connections among customers, distributors, service providers, carriers, and other members of their ecosystem.
For more information on how Synatic’s Hybrid Integration Platform can enable your business, contact us.