Alveo Blog Data Quality

Breaking Down the Barriers

Data Quality and Data Integration Prerequisites to Merge Analytics and Data Capabilities

For the financial services industry, data is a goldmine. It can provide new insights that give a competitive advantage and play a crucial role in answering critical business questions. But like all precious metals, how effectively you mine it and then use it determines the extent of the reward. Collecting data for its own sake is of little use.

To get to new business insights, firms must draw on an increasingly broad set of data, integrate it with internally sourced data and overlay it with their analyses. Easy modelling and onboarding of new data sets are vital requirements here, tying for first place with data aggregation capabilities. Today, many firms have limitations when it comes to joining together datasets or onboarding new data sets. This results in delays in making data operational or adequately utilizing the information already there.

Recent research commissioned by Alveo found that nearly two-thirds (63%) of data scientists in financial services firms say their organization is not currently able to combine data and analytics in a single environment. That’s a severe concern when data can only be maximized when it’s closely linked with analytics. Throw in issues around data quality, volumes of data siloed in data stores and hard-to-access legacy systems that are often hardcoded to specific data formats, and it’s easy to see the scale of the problem.

Ensuring data quality

Data quality is improved when processes and business users use it, and feedback is incorporated to enhance sourcing and screening policies. Insufficient or incorrect data can be damaging, leading to inaccurate analytics, mispriced products and erroneous strategies, to name but a few. Failing to meet strict regulatory demands on data has severe consequences, including reputational damage, financial penalties, or suspension.

Ensuring high data quality is not a one-off project but a continuous process that must start with understanding and validating data. Only then can a robust data quality framework be developed to identify points where quality problems can occur.

The challenges financial firms face with data stretch well beyond ensuring quality. 38% of survey respondents say integrating structured and unstructured data is one of their main challenges. 28% of data scientists say that time-consuming data searches or double sourcing are their top issues. More than three-quarters (77%) say their organization requests the same data multiple times from a single data vendor, leading to unnecessary costs.

A lack of communication and streamlining data sources is also highlighted by 82% of respondents, who say their organization’s front office teams use different vendors than their compliance, risk and operations teams, leading to potentially expensive inconsistencies in market data.

The move to data-as-a-service

Blending data management and analytics can help users leverage multiple data sources and data types. Firms are now moving analytics to where the data lives rather than carrying large stores of often siloed data over to the analytics function.  Financial services organizations want to use these new, integrated capabilities to drive better-informed decision-making. When combined with the latest analytics capabilities, the move to data-as-a-service (“DaaS”) is helping to streamline data sets for operations and provide quality input for analytics.

Combining data management and analytics has vast benefits for quants and data scientists, with 27% highlighting ‘improved productivity as one of the main advantages. Firms and their data scientists can access multiple data sources and also multiple data types. With the help of popular programming languages like Python, users can create a robust and scalable meeting place across their data supply chain to share analytics and develop a common approach to risk and performance management and compliance.

Future focus

Today, technology, process, macro-economic factors, and business awareness contribute to the drive to bring analytics and data together. Financial firms need to see the complete data story for business and operational decision-making.  Bringing data together in one management system offers a new world of opportunity, where costs are optimized, users have better access and visibility on available data sets, and the overall value of the data and the data management function is maximized.