Are you a data hoarder? How to become data-driven

Explore solutions to turn fragmented data sources into a centralized powerhouse for decision-making.

Imagine you’re a data architect at a rapidly growing CPG company. You pull in data from diverse retail partners like Kroger, Amazon, and Walgreens. The data comes from in-store and digital sales alike. And because you’ve acquired a number of smaller brands over the past few months, you’ve added new datasets to your pool, new systems to your pipeline, and new SKUs to your portfolio. 

Clearly, your team has access to a lot of valuable data. But is your data getting to the supply chain team on a regular basis? Is it available to the sales team on demand and structured for easy analysis and action? Can the marketing team access what they need to strategize targeted campaigns and measure performance?

Having masses of retail data and actually putting it in the hands of your users are two very different things. Without cleaning, consolidating, and centralizing the data for company use, it remains fragmented, inconsistent, and stuck in IT.

That’s not an effective data strategy; that’s what we call data hoarding.

Having masses of retail data and actually putting it in the hands of your users are two very different things.

Data hoarders, you are not alone

A CPG’s data resources might include syndicated reports, retailer point-of-sale (POS) data, e-commerce data, consumer insights, marketing performance data, weather data, and more. These are all incredibly valuable, but only if they can be sensibly tapped by decision-makers across the business.

Unfortunately, there tends to be a bottleneck between the mountain of data collected and the users who need it to drive growth and profitability. It’s not that the analyst teams are intentionally gatekeeping the resources to keep users from actionable data; it’s that systems and processes to get multi-channel data standardized and report-ready in a scalable way can fall short. 

Data volume ≠ data value

In a typical scenario, data teams work reactively to structure and configure data for individual needs, such as when the supply chain team requests a specific retailer report or the marketing team requests regional sales figures from Target. Without a methodical, automated way to distribute data, IT teams struggle to compile and configure data silo-by-silo and channel-by-channel to meet reporting needs. It’s a slow and unsustainable process.

It’s like the kitchen is prepping, cooking, and serving each team made-to-order meals when what everyone wants and needs is a 24-hour omnichannel buffet of report-ready data.

Without a methodical, automated way to distribute data, IT teams struggle to compile and configure data silo-by-silo and channel-by-channel to meet reporting needs. It’s a slow and unsustainable process.

Omnichannel data strategy fuels the data-driven business

Now, reimagine a company with a different approach to data. What if all retailer data sources were centralized, harmonized, and made report-ready every day? That data from all channels – in-store or digital, Amazon to Wegmans – would be available to all the teams within your organization without delays for time-consuming ingesting, modeling, or troubleshooting.

When hoarding ends and data-fueled growth begins

With fresh, omnichannel data available at their fingertips daily, teams gain actionable insights and make connections and correlations that reduce costs and improve profitability.

What are my most profitable products, and which are my low-performers? Where can I save money, and where should I invest? Where do I have hidden inventory problems? Those answers are now readily available.

With fresh, omnichannel data available at their fingertips daily, teams gain actionable insights and make connections and correlations that reduce costs and improve profitability.

From bottleneck to data buffet: three steps to take:

• Make a plan: Start by creating a comprehensive data map that outlines all your data sources. Identify the key stakeholders and teams within your organization who need access to specific datasets.
• Centralize: The next step is to centralize your data by aggregating it from all sources and ensuring it is clean and standardized. This process involves consolidating data into a single, unified platform that allows for seamless analysis and reporting, such as Snowflake, or Power BI.
• Flip the script: Instead of reporting needs driving data structuring activities, structure and centralize first. If you prep, harmonize, and normalize data as it comes in the door, it’s ready to use on-demand for any reporting need across the business.

Clean house with Crisp

Crisp serves as a data steward to help organizations stop hoarding and start thriving. We handle all the data cleaning, structuring, and standardization, including managing version control to accommodate portal changes and prevent data pipeline breaks. Our platform seamlessly enables master data management to ensure data is analysis-ready for everyone at your business and on your platform of choice. 

Revenue growth and real-time supply chain management start with a data-driven strategy. Reach out today for a demo.