The Power of Big Data for Startups


These days, the National Football League, BMW, and Uber have something in common — they’re all experimenting with big data in exciting new ways. The NFL is using big data to help football fans track how fast and how far their favorite players sprint during a game. BMW is using big data to help drivers anticipate upcoming traffic light changes, and determine whether they need to speed up or slow down to make the light. And Uber is using big data to predict where riders want to go even before they get into the car.

Big data is fundamentally changing the way we live and work – especially in today’s digital and hyper-connected world where data can come from so many different sources, including social media, transaction logs, e-mails, voice notes, multimedia, text documents, videos, websites, audio transcripts, stock trades, geo-spatial data, and weblogs. If properly harnessed, this data can be a veritable treasure trove of insights, enabling businesses to make informed decisions based on hard facts and metrics, rather than gut instincts or rough estimates.

Big Data, Small Businesses

Large global companies like Amazon and Netflix use big data to great effect – we all know that. But what about startups? Do they need to consider big data, and is it even feasible for them to do so?

Yes! Big data can help startups understand their markets, products, and customers better than ever. It can also uncover hidden opportunities for growth and success.

A few years ago, big data analytics and tools may have been out of the reach of startups, but today, they have become increasingly accessible and affordable. What’s more, startups, by virtue of their size and agility, are well-positioned to act on big data insights faster and more efficiently than their larger counterparts.

Going forward, big data is likely to become an essential part of business. A 2015 global survey conducted by Forbes Insights in association with Teradata found that 59 percent of executives consider big data and analytics either a top five issue, or the single most important way to achieve a competitive advantage. What’s more, two-thirds of executives report that big data and analytics initiatives have had a significant, measurable impact on revenue.

While the respondents of the survey were mostly from large organizations, the implications are similar for smaller businesses and startups. Big data can be a key differentiating factor, enabling businesses to strengthen performance and profitability. And the time to start mining, managing, and analyzing that data is now.

With that in mind, here are a few best practices for your startup to manage and derive the most value out of big data:

1. Identify the Challenges that Big Data Can Help Solve

Big data isn’t going to magically generate insights. The key is to start by asking the right questions. Do you want to acquire new customers? Do you want to make your operations more efficient? Do you want your sales strategies to be more targeted? Do you want to know how your business is faring vis-à-vis the competition?

Only when you identify the questions or challenges that you need to address and solve, will you be able to get the most out of big data analytics.

A few years ago, a leading Middle Eastern aluminum manufacturer approached MetricStream, wanting to know if they could effectively predict the risk of losses from events such as power outages, supply disruptions, and market price volatilities. Our answer was a resounding yes. Through a combination of analytics and statistical modeling tools, we helped the company run data-based and scenario-based simulations to calculate their value-at-risk in various loss situations, and examine the complete range of possible outcomes. With these insights, stakeholders were able to make more informed decisions about their risks.

The point is that there are plenty of tools out there, and there is no shortage of data which can be harnessed to provide insights that can help your business. However, unless you identify the top two or three areas that you want to address on priority, it will be hard to get off to a solid start.

2. Develop a Big Data Strategy

Many businesses tend to get swept up in the big data buzz – they launch ambitious IT projects to capture all kinds of raw data, only to end up struggling to sort through it with clarity and direction.

At the onset, create a strategy outlining how your organization intends to use big data – both over the short term and long term, along with what this will entail in terms of resources such as costs, time, and effort. Revisit your strategy periodically, and ensure it reflects the organization’s priorities – that it is targeted and specific in identifying how the results of your big data analysis will tie into your company’s broader corporate and strategic objectives.

In terms of execution, my advice is to start with small steps. First you have to walk before you can run. Execute a few simple analytics to see what they can do for your business. Stick to a small sample of data, instead of trying to analyze everything at once. For instance, you could track what people are saying about your product on social media sites over the span of one month, so that you can analyze the data to create more targeted marketing campaigns.

If the insights are useful from your early efforts, you might want to gradually scale up to capture more data points such as website traffic and browsing behavior, as well as inputs from customer relationship management systems and other sources. At this point, after some trial and error, you will be able to apply more advanced analytics that can help you slice and dice through the data to derive even greater and more actionable marketing intelligence.

One last point to note: Big data often includes very sensitive data, such as customer profiles, home addresses, credit card information, and purchase histories. Therefore, it’s important to have robust security controls in place at every stage of your analysis process – especially if you’re considering outsourcing any of your data analysis to a third party.

3. Choose the Right Tools

Let’s face it – big data analysis isn’t something that you can or should consider doing manually, even if you’re just a small startup. You will (and probably already) have a lot of data, so it’s best to start looking for tools that can automate the process of harnessing, sorting through, and analyzing all that data.

To that end, there are plenty of affordable tools out there. Google Analytics, for instance, can help you understand your website traffic better. Kissmetrics tracks individual visitor behavior throughout their journey on your site, while MixPanel measures how people are using your mobile app. Then there’s InsightSquared which analyzes historical performance data to forecast sales. SproutSocial and Hootsuite can help you measure your social media performance and impact.

In other words, the tools you need to derive rich insights from data are already at your fingertips. All you need is a bit of research to find out which tools best suit your organizational needs.

Entering the New Year with Big Data

As you prepare and finalize your business plan and budget for the year ahead, it is wise to consider how big data will fit into the mix. While other day-to-day tasks might seem more important at the moment, as you think longer term, it is important to plan for those investments that can be transformative for your business. So, take the time to pinpoint what areas or challenges your business is looking to address. Then explore what innovative big data analytics tools can help solve those challenges and uncover new opportunities for growth. Success in the new year hinges on being able to look ahead; to create an effective strategy, leverage the right technology tools needed to better understand your business and your customers, and finally, act on the insights you derive.

Shellye Archambeau is CEO of MetricStream, a Palo Alto, CA-based company offering governance, risk, compliance, and quality management solutions to enterprises in the pharmaceutical, medical device, high tech manufacturing, energy, financial services, healthcare, manufacturing, food and beverage, and automotive industries. Follow @metricstream

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