The digital age unequivocally runs on data. With virtually all industries investing more in centralised data management platforms as essential business software and data policies as foundational for operations, it’s also becoming more clear that creating infrastructure for data analytics and other processes is essential for making the most of your company’s numbers.
With the right analytics processes in place, data can inform your business and improve your sales. However, an unfocused approach can leave you drowning in irrelevant datasets and bloating up your company’s cloud storage.
So how can you ensure your data management processes are as robust and high-performance as possible? Here are just a few key data management considerations to help optimise your in-house analytics and support data-based decision-making for your enterprise.
Invest in Process Optimisation for Data Management
Establishing strong data processes is an easier endeavour if your enterprise already has strong existing processes and workflow documentation. This is why more future-oriented enterprises are investing in Certification to ISO Standards like ISO 9001 or ISO 27001 as fundamental to their data process development.
By working with an ISO 9001 consultant, you can make sure your enterprise maintains quality processes, not only relating to data management but also to ongoing organisational development. You can also make sure your data management processes and workflows can more easily be integrated into existing workflows. This allows for your business to adopt and implement data management practices faster, which in turn will help you maintain a more competitive edge in your industry landscape.
Treat Data As A Valued Advisor
Data runs the decisions in most major companies – even Warren Buffet lends his ear to it – so whenever you need advice on a tough decision, or where to improve, look at the data. Beyond just telling you the best sellers, you can see what’s trending up, down, and days where you should be leaning into promoting specific products or services – for example, candle sales get a boost on Mother’s Day.
And the more data you collect across the right demographics and the right platforms, the more high-value insights you’re likely to attain. For example, if you know most of your clients are French, you could do a promotion on Bastille Day.
If you run an online business, you’ll be privy to a lot of this demographic information already via analytics systems like Google Analytics or even social media analytics solutions, but for more info you could always run things like giveaways that require users to do a survey to enter. It’s all about identifying the data sets that would be most valuable for you and implementing the measures that would help you attain those data sets. Once the data is with you, all you have to do from there is just interpret it in alignment with your growth strategising.
Use Automatic Data Analysis Tools
AI, machine learning, and standard mathematical transforms can all be lumped into the group of data analysis tools. Each has its own special place to be used. Because, realistically, no one does data analysis manually to ensure efficiency. So, the main question tends to be which tool is right for which job?
AIs, like your classic ChatGPT chatbot, are great for informing your exploration of data, and giving you pointers along the way, but using it to transform your data automatically will need a specialist. Instead, that’s where you’d use machine learning instead. A program to move through your data files, organise them, analyse, and transform them as the data evolves.
Lastly, are the standard analyse tools. The kind most people use without having to learn very much. Programs like Google Analytics are a great way to start and get a look at your data, and to share graphs and changes with your clients and teams. For most companies, this is the place to start, with an AI chatbot playing backup for when you’re out of your depth.
Educate Your Employees About Data
Not only is data education useful in the sense that employees can optimize their work life, it allows them to consider and collect new forms of data, or apply it in ways you wouldn’t consider in a different position. For example, if a barista knows that matcha lattes are trending right now, they can suggest customers to try it, then when those customers chat to their friends about the matcha latte trend, they’ll mention they had a great one at your store too.
To educate your team about data, there are endless online resources to help them get a foothold into the data sphere, but one helpful tip is to keep them in the loop with what you’re doing. A simple monthly email giving the highlights of the sales figures and what you’ve gleaned from them will keep data front of mind and foster a little teamwork too.
Invest in Scalable and Secure Data Infrastructure
As data volumes grow, scalable and secure infrastructure becomes critical. Cloud-based solutions offer the flexibility to scale resources as needed while ensuring data security through advanced encryption and access controls. Investing in resilient infrastructure supports business continuity and disaster recovery efforts.
For higher level analysis, you’ll want to have specific systems for storing and organising your data. For example, a data lake stores raw unorganised data, while data warehouses are collections of organised data ready for analysis. Between all this, data mesh frameworks is a web of teams and data stores that allow for everyone to have access to the data relevant to them specifically.
Harness the Full Power of your Data with Improved Data Management
By now you’re probably convinced that data runs the world – at least it definitely informs international business decisions – but you shouldn’t forget where data comes from: people. A sales point is only a peek into a customer’s decision-making process. Understanding what leads them to making that decision, and what comes after is the key to customer retention and perhaps even word of mouth referrals, both of which are guaranteed to make you more profits than customer acquisition.
So, beyond interacting with the numbers, poring over those minute details, remember who it’s all about: the customers. Get to know them through your data, see what they love and don’t love in both your product and their lives. And with this newfound knowledge, you can keep elevating your enterprise to greater and greater heights.
