4 Steps to Start Powering Productivity With Data

Productivity is one of those buzzwords that is so ubiquitous that it can begin to fall into management bingo but a closer look at what productivity really means in practice and how to improve productivity over time using the power of data is vital to organisations striving for enhanced margins or competitive advantage.

By definition, productivity is “the effectiveness of productive effort, as measured in terms of the rate of output per unit of input.”

So how do you get more… for less (or the same) using data and technology as part of your digital transformation?

We believe there are four foundational steps to start achieving this.


All Your Data – All in One Place

This may sound like an obvious place to start but if you were to reflect on the sheer amount of data sources and list the systems and depositories across your business you might be surprised.

Getting all of your current data in one place may seem like a mountain to climb – but it’s the single most important step you can take to start making data-driven, confident decisions and then measuring their success.

The process for doing this is called ETL. Extract, Transform, Load.

This is the process of extracting data from a source or multiple sources, transforming (which can include cleaning, deduplicating, naming, and normalising) the data, and then loading it into a data warehouse.

This process aims to bring together different data sources from across your business and, from a supply chain point of view, be able to analyse and mine this for insightful knowledge to then drive wiser decisions.

We’ve been working with clients to help this process, blending first-party supplier data with performance metrics, third party credit and finance-related data and other sources against a singular supplier profile. This way, your team gets a 360-degree holistic view of suppliers and can make strategic decisions to increase the work with suppliers, explore performance improvements or to red-flag major challenges that could hinder your business growth – like credit issues.


What don’t we know? (What would be nice to know?!)

At this point, it’s worth asking some open questions about the data strategy too.

What data would be nice to have? What decisions can’t be fortified with data at the moment? Where do we feel exposed to risk or anecdote?

What don’t we have? Where can we get this from? How do we join, merge and blend this data to enable our teams to achieve their goals?

With these open questions on the table, it encourages a more collaborative and solution-focused approach to the overall productivity of the business. If your time has more visibility on certain key performance indicators – they’ll be in a better position to make less risky decisions on the supply chain.

Re-Think Your Metrics

When you’ve got all of your data in one place, and identified the potential gaps in the knowledge base, another stage of reflection is essential to put your team in pole position for data-driven decision making.

This is rethinking the metrics you have within the business based on this new information.

Not all suppliers are created equal and the type of supply, business criticality of their product or service and risk profile of the nature of supply all come into play.

What this means is, you may need to re-think ad realign your metric set for different categories of supply based on data that you have now. A healthy reflection and redefinition of what good looks like what you’ll be measuring and monitoring and how your processes of supplier interaction will be affected all fit into this step – which is more of an ongoing, continual improvement process rather than a fit and forget!


Guiding Growth with Data

The final step is more of a cultural adaptation than the previous numerical and quantitative processes, but one that we felt belonged in this list.

Having data at hand is essential for ambitious companies looking to boost productivity and profitability and competitive advantage but the practice of using data has to be embedded into the company culture.

In some organisations, the data can be undermined by bias or anecdote or the curse of the HiPPO (the highest paid persons opinion!)

Once you’ve started steps 1-3, it would be a massive disservice to then dispel the data with myths of what happened in the last supplier meeting with XYZ, or base it on logistical issues from 3/100 deliveries that just so happened to cause a slight project slip with a tricky client!

This relies on encouraging a different type of critical thinking amongst your supply chain team too, which may need nurturing and training.

It’s balancing risk and rewards, carrots and sticks, improving current suppliers to do their best work or meet your needs (if they know what they are!) or looking for new suppliers to fill the hole. These decisions are often multifactorial and complex so underpinning them with some element of certainty from a wide array of data is immensely helpful.



Embedding data into the everyday operational processes is one of the most transformative exercises businesses can do as part of a digital transformation.

It’s also not simple nor a one-time fix.

But if your business truly wants to get more for less, increase productivity and profitability through supply chain management and make better data-driven decisions that only improve over time – then it’s time you considered these 4 steps.

You may also want to look for simple-to-implement technology solutions that enable these practices too. We’d love to show you how Sourcedogg helps you bring your data together in one place, identify missing parts of the puzzle, connect and blend data sources, then work to achieve your re-aligned metrics.

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