Making Data Analytics Work: Best Practices for Success

In today’s world, companies are all-in on analytics, convinced that data is their golden ticket to success. But despite the enthusiasm and hefty investments, most analytics projects don’t meet expectations. Why? Because many companies lack a clear strategy and follow best practices.

Prashanth Southekal, a business analytics expert, aims to change this dynamic. He highlights three crucial practices and seven general rules that can transform your analytics efforts from good to great. Let’s dive in and make this complex topic simple and actionable.

Three Bedrock Practices for Analytics Success

1. Take an Analytics View of Data
   • What It Means: Align the business questions with the data needed to answer them. The type of data you have will determine the analytics model to use.
   • Example: If your goal is to understand customer preferences, detailed transactional data (like purchase histories) is more relevant than compliance-related documents.
   • How to Do It: Classify your data into three types:
     • Reference Data: Categories like currencies or business units.
     • Master Data: Information about entities like customers or products.
     • Transactional Data: Events like sales transactions or payroll runs.
2. Source Data Strategically
   • What It Means: Don’t wait for perfect data. Use what you have and find ways to supplement it.
   • Example: If your sales data is incomplete, consider purchasing additional data from external providers or using open-source data.
   • How to Do It: Balance the cost of acquiring data with its value. Use techniques like sampling or feature engineering (using machine learning to enhance data) to make the most of your existing data.
3. Move from Analytics Projects to Analytics Products
   • What It Means: Shift your focus from short-term projects to long-term products that continuously deliver value.
   • Example: Instead of a one-off report on quarterly sales, create a dynamic dashboard that updates in real-time and is used across the company.
   • How to Do It: Treat your analytics initiatives as products with dedicated teams for continuous improvement and collaboration.

Seven General Rules for a Successful Analytics Journey

1. Tie Stakeholder Goals to KPIs
   • What It Means: Align analytics efforts with stakeholder goals and establish key performance indicators (KPIs).
   • Example: If a stakeholder’s goal is to increase customer satisfaction, identify relevant KPIs like net promoter score (NPS) and track them.
2. Build High-Performance Analytics Teams
   • What It Means: Create teams that go beyond just having technical skills. Embrace a culture that values data-driven decision-making.
   • Example: Form cross-functional teams with data scientists, business analysts, and domain experts who collaborate closely.
3. Focus on Descriptive Analytics and KPIs
   • What It Means: Start with basic analytics to understand historical trends and performance.
   • Example: Use descriptive analytics to find out why sales dropped last quarter. This builds a foundation for more advanced analytics.
4. Integrate Compliance into Analytics
   • What It Means: Ensure that your analytics practices comply with regulations and internal policies.
   • Example: Regularly audit your data practices to meet GDPR requirements and protect customer privacy.
5. Continuously Refine Analytics Models
   • What It Means: Regularly update your models to reflect changes in the business environment.
   • Example: If your company acquires another business, integrate the new data and adjust your models accordingly.
6. Support Analytics with Governance
   • What It Means: Establish processes for consistent data management and quality control.
   • Example: Create a data governance framework that defines data ownership, standards, and access controls.
7. Use Data Storytelling to Promote Insights
   • What It Means: Present data in a way that is engaging and easy to understand.
   • Example: Instead of showing raw numbers, use visualizations and narratives to explain how a new sales strategy boosted revenue.

Making It Practical

To put these practices into action, start small. Pick a specific area in your business, like marketing or sales, and apply these principles. For instance:

• Identify the right data: Use sales data to understand customer preferences.
• Source additional data if needed: Supplement with market research data.
• Create a dynamic dashboard: Develop a real-time sales dashboard.
• Engage stakeholders: Regularly share insights with your sales team and adjust strategies based on feedback.

Remember, the goal is progress, not perfection. Keep refining your approach, and you’ll start seeing the true value of analytics in driving business success.