Negosentro.com | What Startups Need to Know About Data Analytics | Most startups, especially those in the digital realm, are incredibly interested in data analytics. In a world where data has been named as the new oil, everybody wants to seize the benefits that data can bring to their company as the benefits can be huge.
Proper data analytics doesn’t just lead to having a greater understanding of your business, it turbo boosts growth, allows you to drive sales, retain customers, cut costs – pretty much any decision that you make as a business manager can be enhanced by data analytics.
It sounds so simple – analyze all the data you can and watch your metrics skyrocket. Needless to say, nothing in life and especially in business is ever that easy. Data analytics can be used as a catalyst for a startup becoming an established company, but it needs to be done properly. If you’re thinking of implementing analytic techniques, here’s what you need to know.
1. Without the Right People, Data Can’t Save You
A decade ago, the average business could monitor their data on Microsoft Excel effectively, perhaps with one or two people having data analysis as their priority but not sole responsibility. Nowadays, you need a proper data analytics team to look at your data, as the size of databases as well as the rise of machine learning means that Excel just can’t cut it anymore.
Depending on the size of your company, you might want to outsource your data analysis, but you need to be selective. The right people will have Excel skills, of course, but they will also be highly proficient at the programming needed to properly utilize data: Python, R, SQL and more can be leveraged to find the most effective data analysis technique. Make sure they know your company very well before you hire anybody and compare your needs to their experience to make sure there is as much alignment there as possible.
2. Nail Your Data Strategy
You can have the best data team in the world – you could use the entire team behind Palantir – but you will not get results if you don’t have good data to work with in the first place. It’s not good enough to have vast quantities of data, you need to have the right quality of data.
This means having a data strategy – how will you collect the data you need and what do you need in the first place? Work backwards with your data analysts to strategize how you can get results while minimizing collection and maximizing data quantity. If you have pools of data for every single facet of your business, your storage costs will be unnecessarily high and you might be collecting datapoints you won’t ever use.
3. Make Tech Decisions ASAP
Part of your data strategy needs to include making key technology decisions, which you should do early. Once you know what you want to achieve by using data analytics, you will be able to settle on what sort of infrastructure you need (whether that is a No SQL or a traditional SQL database) and prevent confusion and issues arriving at a later date. You might even find technologies that eliminate the need for a dedicated data team.
Digital Asset Management is a good example of this – if you want data analytics in order to analyze a database of assets and facilitate things like AI image recognition and auto-tagging for internal productivity reasons or large marketing campaigns, there are tools like DAM software that can eliminate the need for a data team.
Likewise, if you want to use data analytics to manage your customer data, there are some CRM platforms that already have AI options – including some main players.
Your data strategy is key to making sure you’re not throwing time and money down the drain by building tools instead of using pre-existing software.
4. Define Results and Start Monitoring
Before you deploy your data analytics program, it’s vital that you have a plan how you will measure your results. What will success look like? What’s an effective team? What sort of ROI do you need to achieve for the program to be worth it? Your data analytic team should be able to help you with this, but if you need to, make sure you have result definitions in terms that all key management figures can understand; keep in mind that the final decision is in your hands and your data analytics team will be biased towards data analytics as a solution. If an alternative with a higher ROI exists, seriously consider your approach.
5. Growth Hacking
Growth hacking has become a bit of a buzzword, but it is one of the most common reasons that a startup will want to do data analysis. When you’re at an early stage where you can pivot incredibly easily, you will want as much information as possible so you can keep moving in the right direction. This is part of acting lean and delivering just the features that your users want so you can avoid wasting any time and energy at all on features that people don’t want as much.
It is perfectly reasonable to want to get a data analytic team just for growth hacking, but make sure they are well-experienced in it. You should still have a data strategy, but it will be less specific than if you wanted to achieve individual business goals. You should still define how you want that growth to manifest – increased purchases, increased customer base, increased customer loyalty or a combination of the three? The typical growth hacking strategy looks at finding low-cost alternatives to traditional means – typically in marketing, but also at the core of the business offering.
If you are implementing data analytics for the purpose of growth hacking over some other business objective, you will need to validate ideas at every stage. There are very short development cycles in growth-hacking and you need to make sure that every decision you make has been validated not just by the data in one instance but in a series of experiments.