If you are a startup business, or you are thinking about starting a business, it is not an overstatement to say that data – capturing, storing, using – has to be at the core of your decision-making.
“The most interesting thing about the technology being built in the startup world is the amount of data it produces. Websites capture data at every click of a user. People who are able to measure, manoeuvre and find insights in that data are invaluable to early stage companies, because they can quickly realise what’s working and what isn’t. “The thing about data is that you really can’t make a significant decision these days without it.” [source: Forbes – The Four Skills That Will Get You Hired By A Startup]
Data helps to provide concrete numbers, backed by insights on consumer behaviour, to drive areas like marketing, advertising, operations, inventory management, and customer service. Big data analytics provide the evidence needed in order to make informed decisions when reaching out to the right target market for launching products or services. Over the long term, data informed decision-making leads to better return on marketing investments.
Some of the biggest and fastest growing businesses, such as Uber and Spotify, put data science at the very heart of their operations. Uber makes use of data science to analyse where customers want to go so that they can increase their staff in line with geographical demand. Even if you’re just starting out, collecting small amounts of data, it is vitally important for you to treat data just as important as everything else. . Better your data collection means better data analysis and insights, and in a highly competitive market, this can mean the difference between make or break. For most businesses starting up now, the volume of data, and the analysis needed to make sense of it, means having access to some qualified data resources, i.e. Data Scientists.
The Importance of Data Collection
While data science is becoming more vital for startups, having a full-time data scientist in-house could be considered an expensive resource. So, what are the other options – how can startups ensure they stay on top of their data game? The first step is to recognise the importance of data, and how it should be collected, stored and used. This is something that needs to happen in the early stages of any startups. Consider first the type of data you need to analyse, and then build the infrastructure to collect it. A good, cost-effective place to start is Amazon Web Services – a cloud-based data ecosystem. Cloud-based systems like Amazon Web Services can handle most of your needs without breaking the bank, however you’ll still need some skilled data wranglers to work on the output, but you won’t need to invest in expensive data infrastructure.
High Impact Projects Need Data Scientists
If you’re working on a larger -scale, or more complex projects, you need to engage qualified data science experts. We’ve worked with many early and late-stage startups on a range of data projects. One of our clients – energy sector startup Electron asked for our help in fetching and visualising complex blockchain data sets in order to build a secure, decentralised and open platform aimed at the energy market. We built a bespoke application to fetch information from the blockchain and create visualisations of Electron’s contracts which improved their business processes and help accelerate their go-to-market strategy.
5 Tips To Ensure Your Startup Is Data Ready
Even if you’re not at a stage where you need to engage data scientists, there are steps you can take to make sure your startup is data ready:
1. Track Your Data
The first step to making use of your data is to track everything. Any piece of data that can be collected on your users should be done regularly, down to every action and interaction. By collecting all of this data you will be able to sift through this information to find valuable insights. It is easier to discard the data you don’t need than go back to collect data you missed.
2. Pull It Together
If you’re tracking data from multiple sources, both internally and externally, you’re going to need to put it all together in order to make use of it. Depending on your business model this can be simple or complex. Larger businesses with separate departments like marketing and finance will generate data from their daily interactions with users. If you want to use the data from these interactions to make informed business decisions, you’ll need to compile it all together. Applications like Google Analytics and Amazon Web Services are good places to start. Only once all of your data is in one place will you have the complete picture, and be able to gather real insights into your business.
3. Build User Models
Once you have got the data down in once place, the next crucial step is modelling. User Modelling helps businesses better understand their customers’ behaviour, wants and needs. It is also the stage that may require the input of a qualified data scientist. However, this is a solid investment as it is where you data efforts will really pay off – you’ll be able to tailor your user experience to your target audience with complete accuracy. It can help shape not only things like user journeys or user experience, but pricing, sales and marketing. For example, you will be able identify with a high degree of confidence users who are likely to convert, and provide targeted pricing or incentives to users who are on the fence. At this point, you are not just responding to demand, but starting to manage it as well.
4. Manage your Metrics and Models
The next step is to manage your metrics and models so that you are delivering on your data strategy. The best data strategy in the world amounts will be ineffective if no one is monitoring and measuring performance. As a start-up you need to make sure that all your employees clearly understand your metrics and models. Whilst it may seem counter intuitive, sharing as much data as possible with your teams makes you a more efficient and effective startup.
5. Think Outside the Box
Finally, follow your instinct and think outside the box. While you should always look to the data, don’t discount the power of gut-instinct. Use these hypotheses as a start point, and use the data to test. Always be looking for ways to improve your data analytics, and how you are managing your metrics and models. If you keep testing and experimenting with your analytics there is always the chance for you to stumble across new insights and improve your business processes.
