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8 Strategies To Capitalize On Your Startup’s Data



by Howard S. Friedman and Akshay Swaminathan, authors of “Winning with Data Science: A Handbook for Business Leaders” (Columbia Business School Press)

Getting the most out of your investments in data can mean the difference between sink or swim for entrepreneurs and small business owners who are focused on growing their businesses. While data is not a magic bullet, it can be a powerful weapon in your entrepreneurial arsenal. By understanding and harnessing its potential, you can make informed decisions, optimize your operations, and position your startup for long-term growth and success.

But where should you start? The list of possible data science investments is endless, but time is tight and money is usually even tighter.

We’ve consulted with dozens of companies on data strategy and organization and the most successful businesses make a data plan early, and regularly evaluate, iterate, and build on that foundation as they grow.

To get you started on that path, here are the top 8 successful strategies for capitalizing the value of your business data – at startup and beyond:

1. Have clear data-related objectives to solve problems.

Unless your business is specifically about monetizing data assets, you should think clearly about how data will be useful in meeting your business objectives and solving key problems.Whether it’s improving customer satisfaction, increasing revenue, optimizing operations, or enhancing marketing strategies, having a clear vision will guide your data initiatives.

2. Collect relevant data.

Every interaction, transaction, and click may hold invaluable insights into your customers, market, and operations, but you need to gather it ethically and securely, and leverage its power to inform crucial decisions. Moreover, there are a vast number of external data sources that can be leveraged from publicly available data sources for free or from private data vendors.

There is a cost associated with data acquisition, storage, incremental data analysis, etc. so use your resources wisely because not all data is equally valuable. Identify what data points are critical for your business goals, whether they are tracking customer preferences, understanding website traffic patterns, or conducting competitor analysis. Focus on collecting and analyzing relevant data efficiently.

3. Invest carefully in talent.

Remember “no data, no model.” You don’t want to collect data without a plan or model for using that data effectively. What that means practically is data infrastructure and those who can create that infrastructure should be are a priority. Hiring a data science team may be prohibitively expensive when your business is starting out, so  a consultant or consulting team may be a cost-effective first step. To make sure you’re getting the right people to the table, focus on the skills you need, rather than the job title. Job titles are often confusing, and quite frankly, “data scientist” has come to mean everything and anything.  But, “data engineers” and those who can build the data infrastructure are critical early hires.

4. Be a good data science customer.

Whether you are using in-house data science resources or hiring external consultants, it is important that you develop a customer mindset and treat data science projects as just that — projects with defined roles, processes, and goals. This means you should track your investments of time and money, set schedules and deliverables, and define how you will measure success. Adopting a customer mindset also means accepting that you do not need to be an expert in data science, but you do need to communicate critical information and set expectations for your team. Familiarize yourself with the basics — how data is collected and stored, what sort of algorithms or clustering models are used to find patterns in data, etc — so you can make useful contributions and set guardrails for the work.

5. Build on successes.

Take an iterative approach to data initiatives by starting with small, manageable projects, analyze the results, learn, and then iterate. This allows you to extrapolate lessons from each step and adapt your strategies accordingly.

It is tempting to chase after the hottest topics whether that be Generative AI or deep learning but starting with solid descriptive statistics, exploratory data analysis develops an understanding that will help any advanced exploration.

6. Ask questions.

When working with data science teams, ask questions.  Make them explain what they are doing and why – in a language that you can understand.  Questions can and should be asked across the breadth of data issues.  This spans the gamut from the tools the data science team uses to the basic analysis steps taken to spotting biases to model building to data protection to ethics to privacy concerns. If the team can’t answer basic questions about how data is being used or what tools they are using to analyze the data or build models – this is a big red flag.

7. Prioritize ethics, including data privacy and security.

The last thing you want is to be a B-school case study on how to destroy a business due to poor data security.  Data breaches can be devastating for startups. Building biased models is not only an ethical issue but also one that can be avoided if companies prioritize ethics. This means that you need to ensure that robust protocols are established to protect customer information and adhere to data protection regulations.

8. Embrace data-driven decision making.

Cultivate a culture of data-driven decision-making within your startup. Encourage employees to use data in their decision-making processes, from product development to marketing strategies to risk assessment to improving operations. This ensures that decisions are based on evidence rather than intuition alone.  Encourage data analysis throughout the organization to understand what works, what doesn’t work, and why.

This list is not meant to be a cookbook for success.  Rather it is meant to give you a guide for some critical items to prioritize in your company’s data science journey.

The landscape of data is vast and constantly evolving, offering endless opportunities for growth and innovation. By embracing the strategies outlined, you’re taking a crucial step towards unlocking the full potential of your business. The knowledge you acquire will uncover new ways to understand your market, enhance your operations, and connect with your customers. Embrace this journey with curiosity and enthusiasm, and you’ll find that the world of data science is not just a tool, but a gateway to transforming your vision into reality.

 

Howard Steven Friedman is a data scientist, health economist, and writer with decades of experience leading data modeling teams in the private sector, public sector and academia. Akshay Swaminathan leads the data science team at Cerebral and is a Knight-Hennessy scholar at Stanford University School of Medicine. Together they are authors of “Winning with Data Science: A Handbook for Business Leaders” (Columbia Business School Press).




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