As organizations enter a new year, leaders across industries are increasingly collecting more data to drive innovative growth strategies. Yet to move forward effectively, these organizations need greater context around their data to make accurate and streamlined decisions.
A recent Data in Context research study found that more than 95% of organizations suffer from a data decision gap, which is the inability to bring together internal and external data for effective decision-making. This gap imposes a number of challenges on organizations, including regulatory scrutiny and compliance issues, missed customer experience opportunities, employee retention problems, and resource drainage due to increased manual data workload.
While the influx of data is endless, organizations that fail to obtain a holistic, contextual view of complete datasets remain at risk for ineffective decision-making and financial waste. However, with the proper systems and technologies in place, companies can overcome the data decision gap to foster success in 2022.
Siloed Systems Create Fragmented Data
Fragmented data and disorganized internal systems have plagued companies for years, making it difficult for organizations to harness the full potential of their data due to a lack of context. Information technology has also drastically evolved, presenting companies with hundreds of different applications to choose from for storing data. However, this range of multiple siloed systems can create disparities in data.
For example, financial services organizations might utilize different systems for each of the products they offer to customers and those systems might not be joined together on the back end. When trying to make informed decisions about a given customer, financial services professionals will need to consider all the available data on that customer to take the right course of action – but they can do so only if they are able to look at that data holistically. Without a single customer view in place, financial and other institutions might struggle to address customer needs, creating negative experiences.
To combat this issue, organizations need their data to move across systems in real-time feeds. Lags in data processing create missed customer opportunities if employees cannot access the latest view of up-to-date information. However, the right technologies can take fragmented data and make it accessible to individuals across a company, giving multiple employees comprehensive views of timely data.
Outdated Data Impacts Employee Workloads
With data constantly evolving, organizations need to implement effective Data Management systems to ensure employees are equipped with the time and knowledge they need to navigate through data seamlessly. Data can become outdated at a fast rate, and manually monitoring for these changes requires sustained energy from employees, which can prevent them from utilizing their time and talents in more productive ways. This can lead to burnout and generate retention issues.
Tools like artificial intelligence, entity resolution, and network generation can solve this by updating datasets in real time, giving employees more time to manage their workloads, conduct investigations, and pursue efforts to create stellar customer experiences. Not only do these technologies help improve employee routines, but they are also the key to cleaning up data, catching fraud, and enabling organizations to avoid regulatory and compliance issues.
Regulatory Scrutiny and Compliance Issues
The aforementioned study found that nearly half of respondents experienced issues with regulatory scrutiny and compliance efforts as a result of the data decision gap. This comes as no surprise given that organizations are required to have appropriate controls on data, especially in industries like financial services.
Within financial services, regulators are enforcing stricter rules for organizations to remain compliant with their Anti-Money Laundering (AML) and Know Your Customer (KYC) models. While teams may attempt to keep customer records up to date by leveraging different systems, the underlying problem is data lineage and data quality. When regulators see any inconsistencies in a company’s data, they impose costly fines or freezes in operations until the data is sorted, creating major setbacks both internally and externally.
Inconsistencies in data create a lack of trust, which can spark differing views around company operations. This leads to discussions over issues that could have been better managed if a more comprehensive and accessible view of data had been available from the outset.
In a world where data will continue to grow exponentially over the next several years, organizations must work to overcome the data decision gap. Organizations will always face challenges as internal and external circumstances continue to evolve, but by adopting technologies and processes to ensure data is always reflective of the latest developments, they can make the best possible decisions.