Introducing change in the financial sector may feel like you’re turning a large, cumbersome wheel. It takes many hands and does not always produce a n...
Introducing change in the financial sector may feel like you’re turning a large, cumbersome wheel. It takes many hands and does not always produce a noticeable shift, much like the transformation from legacy technology. Yet, to keep pace with the modern-day digital environment, finance teams need to adopt new ways of working with information.
Gaining BI insights – from spreadsheets to dashboards
Employees working with data in the financial sector have traditionally relied on one particular legacy tool. Their go-to solution—spreadsheets. And this might still be standard practice in certain institutions. Practitioners print reams and reams of spreadsheets. Comments are made on the printed pages. Then someone draws up a consolidated report, which is distributed to the analytics team.
Sticking to such dated methods does not align well with the current business status quo and may even inhibit companies from maintaining a competitive advantage. Keeping ahead of the game requires real-time insights, something that spreadsheets alone cannot offer. Actionable insights require cutting edge digital solutions.
Modern BI tools are the key to business progress and survival, especially in a disruptive ecosystem with fintech startups popping up like mushrooms. Data users require a more interactive and intuitive way to get the most value from their data analytics pipeline. Financial dashboards not only make it easier to track key financial metrics such as profit margin, expenses or sales, but also supply real-time business intelligence.
Data governance yields exceptional BI
Data scientists contend with legacy systems, siloed and often dirty data, and reluctant users. So how do they overcome these barriers and gain a semblance of control? By leveraging data governance, the team can grab the reins and control the collection, reporting and management of data in a business.
In the financial sector, this function is crucial for protecting confidential customer information, but also for another notable component – data standardization.
Business intelligence relies on clean, consistent data. And the standardization process ensures the consistency of data throughout an organisation. It helps maintain the quality of data.
Data quality is one of the five pillars of Master Data, or essential business data, according to Scott Taylor from MetaMeta Consulting (aka the Data Whisperer), together with value, structure, connectability and coverage.
Five pillars of Master Data
- Value: Actionable, accurate data provides value to a business.
- Structure: Structure your data around vital business components such as customers, products and assets.
- Connectability: How data flows throughout your business.
- Coverage: Your master data must cover your whole business ecosystem, like extending your data structure when you take on new business.
- Quality: Data quality is measured by accuracy, completeness, timeliness, and uniqueness.
If any of these pillars are missing, the result is bad data that could damage the entire analytics pipeline, and a business’ survival potential.
Financial Dashboard built with Qlik Sense and Vizlib
Continued here: https://home.vizlib.com/2020/02/11/best-practices-in-financial-reporting-the-benefits-of-standardisation/