Managing a startup can often feel like a fight for survival, so no startup leader should overlook the advantages that data can provide. Data empowers every business to map demand, uncover new efficiencies, allocate resources effectively, and identify niches to explore for maximum product-market fit in the wider business ecosystem.
Data is especially important for startups, which typically operate on tight budgets and are still proving themselves within a given vertical.
But managing and applying data is easier said than done. Data traffic is enormous, so you may quickly find yourself drowning in noise, without a single actionable insight or meaningful prediction if you approach your business intelligence activities without a proper data strategy.
Moreover, without a strategy for data governance and privacy, foul play can become hidden in plain sight. You may not be aware of a data breach or an employee’s unlawful access if management of your data isn’t organized.
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Which brings us to the main issues that startups need to consider as they form an effective data strategy.
1- Where to Pull and Store Data
Startups don’t always realize just how much data they already possess. Spend some time thinking about where you draw your data from, including social media and employee work hours as well as more obvious sources like Google Analytics or market research data.
It can be tempting to gather all your data in a data warehouse before figuring out how to open it up to analysis, but that waterfall approach is antithetical to the agility that gives startups their edge. It’s better to think of an “innovation factory” that houses the datasets needed to solve specific problems.
You’ll need to think about how accessible your data should be so it can be processed for different use cases. Robust data controls and governance help you maintain a single source of truth, which keeps data reliable. It’s vital to comply with data storage and protection regulations, but you don’t want to stifle innovation by effectively nullifying key use cases. There’s always going to be a tension between the need to regulate and standardize data for governance, and the need to easily apply it to different use cases for better business decision-making.
2- How to Divide Labor and Institute Scalable Processes
Data management requires cooperation between the IT and business teams, but you need someone at the head of data operations to take responsibility and accountability for gathering, storing, processing, and applying data.
You’ll need to decide between centralized data management, which offers better compliance but more rigidity in data application, and a decentralized approach. The latter is usually better for startups, which need to avoid data silos, and preserve flexibility in applying data to multiple use cases across the organization.
Thinking ahead about the workflows you’ll need to gather and process data allows you to be proactive about selecting platforms, choosing between on-prem or cloud tools, determining data management pipelines and allocating responsibilities. Your data processing workflows should include data tagging, model tracking, and preserving data lineage and metadata so that you can improve the depth of our insights and ensure scalable business intelligence workflows.
3- What Outcomes Your Data Will (Hopefully) Yield
What are you ultimately trying to achieve with your data? As with everything else in startups, the key to finding the best methods and protocols is truly mastering the reasons for the.
In this sense, your startup’s data strategy is useless unless it outlines the objectives that it aims to achieve. To formulate this part of your strategy, it’s often best to use a right-to-left approach, where your logic begins with your desired outcomes, and uses them to guide you to identify the right insights and associated datasets, architectures and processes that will bring you to achieve them.
After all, there’s no way to check if you’ve achieved success unless you know what success looks like.
Many fownders can be overawed by data science and forget that it isn’t an end unto itself – it’s a tool to add value to your business. You can’t wield any tool effectively unless you know what you want to do with it. Identify the use cases that add the most value to your business to define the questions that you need your analytics to answer.
4- How Stakeholders Access Insights
You want to make sure that your frontline employees both trust your models, and can apply data insights to their workflows. Otherwise they simply won’t use them and you’ll miss out on the benefits of running a data-driven organization.
Sync data analytics with your daily processes to make sure your data strategy is relevant to your company’s most common use cases.
Think about which tools and interfaces your employees need to be able to generate and draw on valuable insights independently, without swamping your data team with low-level requests for one-off reports and models. It’s worth it to train employees to access data alone and adjust your culture to be data-centric.
5- What Feedback Loops Are in Place
Best practices among startups today include building in feedback to support CI/CD mechanisms that refine your data strategy over time.
You want to bake in processes for ongoing input from stakeholders, dedicated data wranglers, and database management employees so that you can measure your success in meeting data strategy goals and spot opportunities for improvement.
Like so many other things with startups, the best data strategies are built to be iterative.
Data Strategy Is Non-Negotiable for Startup Success
In 2020, data strategy is no longer just a nice-to-have for startups. Agility, efficiency, and transparency all depend on a successful data strategy.
When you spend some time considering outcomes for your data strategy, data sources and storage, how to process data while guarding its authenticity, opening up access to insights, and building in feedback loops, you’ll be better placed to create an effective data strategy that helps your startup get ahead.