Descriptive Analytics is a foundational stage in the data and analytics (D&A) journey. This part of the journey is where organisations can create detailed reports and dashboards that provide insights into data trends, whether they happened in the past or are current.
This step is crucial for Heads of Departments in Sales, Finance, Marketing and Supply Chain as it creates a path for a strategic plan to start off with properly informed decisions.
The Descriptive Stage is a phase in data analytics where data is methodically gathered, processed and finally presented in a way that is understandable and accessible to those who need to be able to interpret it. The data will depict both past performances and current trends. It provides a foundational understanding of a business’ operations and its work environment.
- Data Overload: Businesses may run into an overwhelming amount of data from daily activities and customer interactions to comprehend.
- Integration and Siloed Data: Data often resides in separate systems which would make it difficult to then have a complete view. This would affect planning and coordination across the company’s departments.
- Technical Complexity: Setting up self-service BI tools, like Microsoft Power BI, can be complicated if not all employees have adequate technical skills.
- Inaccurate Data: If the data used is inaccurate, then the results will also be inaccurate. Business processes must be in place to ensure the data is accurate and clean. This is important as it mitigates errors that can result in poor decision-making.
- Identify key data sources and use an easy-to-use BI tool: For sales executives, identifying products that perform well in specific areas can help streamline marketing efforts. Finance departments can analyse past expenditures and revenues to distinguish budget allocations that effectively affect revenues.
- Make sure Data Quality trains your team: Investing in regular data cleaning and validation is critical. Additionally, empowering your team with BI tools like Microsoft Power BI, and data literacy training can greatly enhance decision-making ability.
- Use external knowledge: Getting guidance from outside consultants for basic design or training can help tailor solutions to your unique business needs.
- Sales: A salesperson improves sales performance by visualising sales profiles across regions and product lines and adjusting inventory marketing efforts based on insights gained from Descriptive Analytics.
- Finance: Through Descriptive Analytics, the finance department of a software company finds a direct correlation between investments in customer support and the number of new subscriptions which guides budget reallocation
- Performance: A B2B tech startup reallocates its marketing budget to email campaigns after identifying their highest engagement and conversion rates through Descriptive Analytics.
- Supply chain: Descriptive Analytics enables a manufacturing company to simplify inventory management, reduce costs and ensure supply chain availability by using historical data on inventory levels, requirement planning, and time management analytics.
- Tool selection and integration: Choose BI tools that integrate well with your existing systems and are easy to use.
- Data Governance: Clear data governance policies should be implemented to maintain data quality and accuracy.
- Interdepartmental Collaboration: Encourage interdepartmental collaboration to ensure a feasible vision and alignment with business objectives.
- Continuous learning and change: Treat Descriptive Analytics as an ongoing process, changing your processes based on regular reviews of reports and dashboards.
For business executives involved in sales, finance, marketing, and supply chain, Descriptive Analytics is not just about interpreting large amounts of data but about applying this data strategically to make informed decisions if it improves productivity and efficiency. By following these guidelines, organizations can lay a solid foundation in Descriptive Analytics, which sets the stage for future advancements in data and analytics.
