How to choose your first data tool?

Introduction

Your startup is growing at an incredible rate and the valuable data you are collecting is becoming more and more abundant. Unfortunately, it's also becoming harder and harder to manage this data in a way that allows for a cohesive and unified view of the data. Silos are forming, meaning that data is becoming fragmented and it's becoming difficult to consolidate it into a single source of truth. You need to find a way to make use of all of the data you have, while also ensuring that it is stored in an organized and efficient manner.

For startups, working with data can be a daunting task. But, when done correctly, it can help companies make decisions faster and more collaboratively. By leveraging the right data tools, teams can quickly track and analyze data to gain valuable insights and make more informed business decisions. In this guide, we'll explore how startups can choose the right data tool for their needs and how to make the most out of their data-driven initiatives.

Things to consider for startups

Data solutions come in all shapes and sizes, so it can be difficult to know where to start. Before selecting a solution, it is important to consider what type of data will be collected and analyzed, the nature of data sources, the size of the team, the complexity of the analysis, and the budget. With these considerations in mind, startups can begin to narrow down their options and select the best data tool for them. Additionally, it is important to consider the scalability of the tool, as data needs are likely to grow over time.

Things are always moving fast in startups, data is everywhere and chaos is just inevitable. Therefore your first ‘startup friendly’ data tool could be incredibly powerful that can help your company to achieve success.

It can offer insights into how to improve the efficiency of business processes, identify problems before they become too serious, and boost customer retention rates as some of the common use cases. Additionally, data can help create better marketing campaigns and track the progress of services and products.

Before you start googling something like “top BI tools”, here are some questions to think about to make sure you have the framework to evaluate the best fit for your company-

What is your data roadmap?

Firstly, it is absolutely essential to accurately identify the roadmap of your current and upcoming data projects, associated business goals and the expected outcomes for each. What are you ultimately trying to achieve by embarking on these data projects? Is it increased efficiency, cost-reduction, customer satisfaction or something else entirely? Careful consideration and analysis must be undertaken in order to develop a solid understanding of what needs to be accomplished and how best to accomplish it.

What are the whereabouts of your data?

What type of data will be most useful to help you achieve your objectives? Are these data points coming from a single source, such as a data warehouse, or are they being collected from various applications? Is there already a data warehouse in place, or do you need to create one to store all the data in one centralized location? If a data warehouse is necessary, what steps will you need to take to ensure it can handle the data you need to collect and store? Additionally, what type of processes and procedures need to be put in place to ensure the data is accurate, organized, and up-to-date?

What works best for your team?

What does your team look like? Do they have a variety of different skills and backgrounds? Are they highly technical, with experience in coding and programming? Or do they have more of a semi-technical background, with a focus on analytics and data analysis? Or is your team purely non-technical, with an emphasis on creative problem solving and communication? Knowing the level of technicality of your team can help you decide which analytical interface might be the best fit for your company. SQL IDEs, NLP, R/Python, drag-drops, or a combination of all are all excellent options depending on the skillset of your team.

Airbook can help- How?

At startups, teams tend to ‘self-organize’ information first in a way they are best able to consume it and then collaborate within their own circles of trusted humans to validate decisions.

Notion, Google Docs, Slack, Figma are all modern tools that create seamless experiential layers to allow humans to organize information in their own way and collaborate to gain consensus (e.g. polls), enable majority voting (e.g. upvotes), make autocratic decisions and ask for consent (e.g. approval processes).

However, all these methods require an ability to understand, share context and adopt one method over another with awareness!

When it comes to the data world, we at Airbook understand the need to have a streamlined process that allows consumers and operators of data to remain in one system without having to switch over to another. To this end, we are building Airbook as a collaborative experience where revenue and data teams can ‘co-work’ and make decisions together, guided by the insights they have gathered.

Often, startups have data in silos and find it difficult look at data from a holistic perspective and we are striving to break these silos by introducing a modern solution that brings people together to work with data. Airbook provides teams with ‘one page’ that consolidates all the essential elements needed to make data-driven decisions- from gathering requirements to acting on insights. Teams can gather and iterate on data requirements, explore and visualize data via drag-drops, SQL, R/Python or NLP, build trust by making the underlying business logic visible, give context to the numbers they see and explain insights, and brainstorm on the next steps to take and build an action workflow.

The ultimate goal of Airbook is to provide a single, collaborative workspace for teams to work together and quickly move from data exploration to making decisions. By utilising our platform, teams can save time, money and resources, making data-driven decisions in a fraction of the time.

‍