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  • Rahul Rao

Six ways your organisation can make better use of its data

It is well known that most organisations don't make the best use of their data. With the mountains of data available and the bewildering number of options for data processing and analysis, it can be difficult to know how to make your data work for you. Here we offer some simple tips.


1. Align data strategy to organisational goals

Data collection and storage is currently cheap and getting cheaper, but the amount of data available is growing incredibly quickly. You don't need to collect all the data you can - instead figure out what your organisational goal is and decide what data is going to help you achieve that goal. If you're looking to break into the travel industry, you may want to gather data on popular travel destinations. If your next expansion is into the supply chain industry, you will need different data.


2. Use data warehouses

Data warehouses are a relatively new concept in cloud computing. They are designed to be able to collate data from different sources, perform whatever pre-defined processing and transformation is required, and serve it to users at varying levels of granularity. Data warehouses promote reusable features, a single gold-source of truth, and automated, scheduled processing, leaving your people free to visualise, draw insights from, and act upon the latest, best data you have available.


3. Democratise data

Sure, you have a cadre of elite data engineers who can extract and transform data from several systems. Maybe they also understand statistics - that's good. And maybe they also are subject matter experts - they're treasure and you should hang on to them for dear life!

For the average company who can't find these superhuman employees, you need to get your statisticians and SMEs face-to-face with your data. That means getting data engineers to share their data in accessible and usable ways so that people who understand the data don't need to be Python-literate to extract insights. Consider BI software like Tableau or PowerBI, or the free alternatives like Dash. More eyes on your data = more scrutiny and more scrutiny = better quality!


4. Empower your people to innovate with data

Innovation is nearly always a case of investing time now to reap benefits later. You can't afford to invest time now when you're running 100 miles an hour just to keep up. Give your people the training they need to work with and make sense of data. Coursera, EdX, or even simple Lunch 'n' Learn session are great tools for this. Then give them free time every week to invest in dreaming up new ideas. Sure, most of the ideas won't work out but they're worth it for the ones that do. Google gives employees 20% of their time to innovate - and got Gmail, Google News and AdSense out of it!


5. Combine data and machine learning

Human beings are great at spotting patterns in simple data, making it possible for you to get many valuable insights from data. But today, things are much more interconnected than before and there are interactions between seemingly separate events that humans would never have thought to inspect. As a consequence, today's data is much more complex and nuanced than the data of yesteryear, necessitating the use of machine learning techniques to make sense of and draw insights from data. Whether it's a simple decision tree or a complex neural network, ML models augment human expertise tremendously to make tackling complex data a less intimidating task.


6. Measure the effect your data strategy is having on your organisational goals

Set business metrics for your data operation to meet. Make sure the metrics make sense - otherwise they'll be regarded as "hoops to jump through" by your team. Also ensure they're something you can measure with some accuracy - as Peter Drucker says, "If you can't measure it, you can't improve it". Review these metrics at regular intervals and check that they continue to make sense and that your team is tracking in the right direction. And if you start to find that the metrics aren't improving, go back to step 1 - align your data strategy to organisational goals.

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