Big data is having a big moment.
Today, 97.2% of companies are investing in big data tools and the market is growing at a rapid pace. CEOs must make sense of big data to keep their companies competitive in a world that’s becoming increasingly connected and seeing unprecedented amounts of data generated every second.
- Big data can be defined and understood using the 3 Vs: volume, variety, and velocity
- Big data comes in structured, unstructured, and semi-structured forms.
- Big data analytics informs current and future business decisions.
- CEOs can make sense of big data by investing in infrastructure, utilizing the right teams and tools, and cultivating a data-driven culture.
WHAT IS “BIG DATA” ANYWAY?
The 3 Vs
To understand what big data is and how it differs from data in its traditional sense, we can look at the “3 Vs” commonly used to define it: volume, velocity, and variety.
As you can see above, each of the 3 Vs represents a different factor that qualifies big data.
Volume – Big data sets are so large and complex that traditional data management tools can’t handle them. To give you an idea of just how big big data is, consider this: every person generates 1.7 megabytes of new data per second and 90% of all of the data ever created was produced in the past two years. Data at that volume and rate of growth requires more sophisticated approaches and solutions.
Variety – In the past, data was typically generated in text form and could be neatly stored in databases like Excel spreadsheets. Today’s big data comes from many sources and in a variety of formats such as videos, voice files, images, social media interactions and more. It also has different levels of structure and is categorized as structured, semi-structured, or unstructured depending on its format, organization, and immediate analyzability.
Velocity – Velocity is the speed at which big data is generated and processed. Big data processing is almost always instantaneous — think social media engagements, website clicks, and digital payment transactions that process at the same time they occur.
Big Data Analytics
You can’t really know big data without also knowing big data analytics, or the methods by which big data is analyzed and applied. Big data analytics has grown into a field of its own with careers like predictive modelers, statisticians, analysts, and data scientists who use advanced tools and techniques to glean insights from enormous amounts of complex data.
Some of the main types of analytics performed by companies using big data include:
Predictive Analytics – Predicts the likelihood of future events or circumstances.
Prescriptive Analytics – Offers recommendations for the best course of action based on available data.
Descriptive Analytics – Analyzes past data to better understand what happened.
Diagnostic Analytics – Takes descriptive data a step further to analyze why what happened, happened.
HOW SMART CEOS CAN MAKE SENSE OF BIG DATA
Invest in Infrastructure
Big data can only be valuable to companies if they have the infrastructure to store and manage it. This takes some planning and preparation on the part of CEOs and company leadership. Fortunately, the massive migration to cloud computing means that companies no longer have to invest in big data warehouses or costly and inefficient on-site servers to store their data.
Still, companies need to make intentional decisions about key data infrastructure elements including data collection, storage, analysis, and output.
Focus on What Matters To Your Company
It can be an overwhelming prospect for any CEO to understand the full scope of big data and utilize all of its potential. In fact, it’s likely impossible.
To effectively make sense of big data and implement the right strategies, CEOs and their teams must know and prioritize what is most important to their company. What are the most critical things for you to know? In what areas will you look to make your big data actionable (i.e. internal operations, customer experience, etc.) and what is their order of priority?
When it comes to big data, it’s better to focus on a few areas and execute them well rather than try to do too much at once.
Find the Right Tools for Big Data Analytics
The right tools (including the right people) to handle your big data initiatives will be a central part of your success. Big data requires sophisticated tools with better capabilities and bigger bandwidth than traditional business intelligence tools. Investing in big data software tools to manage your data eliminates human error and makes big data possible for your company.
To that end, big data is not something that can be taken on simply by traditional marketers or accountants or employees in any traditional functional role, even if they are used to “crunching numbers.” CEOs should consider who internally will take charge of big data initiatives and consider investing in new roles or big data service providers when in-house resources require additional support.
Make Your Data Actionable
Here’s where CEOs stand to make the most impact. Creating actionable data is the only way you can gain true ROI from your efforts. Without a big data strategy that includes actionability, your company risks falling behind the competition and missing out on valuable insights that could transform your future strategies and business decisions.
When you think about making data actionable, think in terms of direct and indirect action. Your data can prompt direct actions (like an alert indicating an increase in unsubscribe rates that prompts your marketing team to take action) or indirect actions (capturing a specific data point over time to inform future decisions).
CEOs can’t understand all of the actionable potential of their big data alone; you’ll require a team of leaders from across your organization to help you identify places to apply your data insights and strategize around how to do it. This means fostering a data-driven culture where employees embrace data and proactively seek out opportunities for leveraging it.
McKinsey provides a good visual that illustrates how this instilled culture serves as the foundation for all other steps in the big data implementation process.