In my last article, we looked at the role played by application programming interfaces (APIs) in exchanging data easily and seamlessly across systems, and ensuring the right information reaches the right person at the right time. In this follow-up article we turn the spotlight onto how to use data for analytics and insights.
At BoardEx this is something that we’re seeing more and more of our clients do successfully. They’re increasingly using BoardEx as one of numerous data sources for analytics – forming “data lakes” by consuming disparate data sources into a shared location, and allowing various teams across the organization to access and use that data to generate new insights.
When organizations do this, different teams have different use cases. Some may be interested in the most up-to-date data, but are keen to merge complementary data together to increase its impact. Other teams may use it for time series analysis to view how particular metrics have changed over time, and even cross-reference with other metrics to reveal patterns of correlation or cause-and-effect. These patterns are not always easy to identify – so data science teams might use techniques such as machine learning to build a model that encapsulates any patterns in the data and, once these are revealed, use them to generate predictive insights.
Regardless of the techniques that a business uses to perform analysis, it all starts with the data. As we all know, generating data is easy. But capturing it in a location and format in which it can be used is more difficult. And the hardest thing of all is using that data effectively to make insightful business decisions.
Why Become Data-Driven?
But whatever the use cases, the direction of travel for these firms is clear: they’re each looking to become a data-driven business. It isn’t hard to see why. Becoming truly data-driven isn’t easy – but if done right it brings tremendous advantages.
One of the biggest is the ability to measure and respond faster to changes in customer behavior or the market. It could be a new product release (by you or a competitor), a marketing campaign, a news article or tweet, an acquisition or restructuring: anything that has an effect on metrics such as cost, revenue, customer usage and engagement can be measured. If the measured effect can be correlated back to the cause it drives insights, enabling better, faster decisions.
Being data-driven also gives you a clearer view of how your customers use your product and whether it’s doing its job. Many businesses only have feedback from a handful of customers to go on when making decisions about products and services. Far better to have hard data insights that enable you to spot usage patterns and trends as they emerge, and test out new ideas robustly with data analysis before scaling them up.
All of this can help your business stay ahead of the competition and grow, diversify and adapt. At its most effective, smart use of data can generate the “flywheel” effect, where successive metrics feed a virtuous circle of rising value to drive the business forward. Take Amazon – where rising consumer traffic attracts more merchants, helping to drive price competition that in turn attracts more consumers, with every stage tracked and managed through data and analytics.
Being data-driven isn’t only about generating more revenue. Firms can also turn the microscope on themselves to improve operational efficiency through data gathering and analysis. From reducing operational costs to better management of people, any problem has a potential solution with the right data to drive decisions.
Challenges Along the Way
We mentioned earlier that becoming a data-driven business isn’t easy. So what hurdles need to be overcome? One of the biggest can be culture: data is everywhere, but in many organizations the behavioral norm is not to use it, favoring “gut feel” and assumptions over evidence.
Another challenge is that organizations often create – or inherit through acquisition – data silos distributed across the business. There may be the same or similar data in multiple places, raising questions over accuracy and ownership. Worse still is complementary data existing separately, which if brought together could yield valuable insights.
These issues apply to data already within the organization. But data is a commodity and can be purchased externally to complement and enhance existing data. A true data-driven business considers every option for getting the insights it needs.
Three Steps to Become a Data-driven Business
In our experience, the journey to become data-driven has three key stages.
Step 1: Understand what data you have and put the basics in place
This step begins with clarity on what matters to your business: your strategic goals, challenges, KPIs and – if relevant – what drives your flywheel. You should then identify and explore the data you already collect, and seek out and address any silos. You can also identify potentially complementary data and how it might be correlated to identify cause-and-effect: perhaps compare customer usage data and product release data to see if a product improvement had the desired effect. Picking this kind of low-hanging fruit is a good way to start building a data-centric culture – and then you can increase the momentum by introducing some basic but powerful tools and building teams around individuals who are on board with the idea of using data.
Step 2: Get serious about analysis
With the foundations in place, now is the time to start thinking about introducing some data analysis infrastructure. Maybe your data has become unmanageable, with a combination of structured and unstructured data held in many separate data stores. Suppose you have sales and customer account data in Salesforce, marketing data in Marketo and customer usage data sitting in Amazon S3. How can you manage the distribution of all this data?
Let’s look at the options. Data warehouses have been around for many years, and more recently data lakes. These terms both describe a centralized location to store data from various sources so it’s easily accessible to data engineers, data analysts and decision-makers. The difference? The former tends to hold structured data that has already been processed for a specific purpose, whereas the latter tends to hold a pool of raw or unstructured data with potential use.
At this stage of the journey, creating a data warehouse is the most appropriate step. Structured data with a clear purpose is much easier to deal with. And there are many data warehouse service providers in the market, with different attributes such as performance, cloud vs on-premise, scalability and pricing models.
Data warehouses typically have analysis engines built in, but many data-driven organizations use multiple technologies together to create a hybrid solution. For example, extracting data from your data warehouse into a business intelligence tool such as Tableau means you can take advantage of advanced visualization capabilities and get analysis that’s easy to consume and understand to the people that need it, inside or outside the business.
Finally, if you’re really getting serious about analysis, data hygiene must be a consideration. Inaccurate, out-of-date or conflicting data must be cleaned up regularly. Inventory is also key: knowing what you have – particularly if you’ve begun to form data lakes of potentially useful data – is essential.
Step 3: If you don’t have the data, someone else probably will
Need more data to refine your insights? It’s probably out there. At BoardEx, we’re renowned for our data quality: for over 20 years we’ve been gathering data on business leaders and their organizations. To meet our customers’ needs, we regularly supplement this huge core of information by going to the market to buy third-party data to enrich our products and services. Data exchanges and marketplaces such as Snowflake and others offer a treasure trove of useful data that can be consumed right off the shelf.
BoardEx is both a data provider and a data consumer, so we also know what our clients need when it comes to consuming our data. Using the BoardEx Data Feed, our customers enrich their analytics with highly accurate and complete data on the top global business leaders. The use cases for this kind of data are wide-ranging and increasing all the time.
Conclusion: Embarking on the Journey
As businesses, we all generate data and even use it to make decisions – but this process is often ad-hoc and using siloed data sets. A truly data-driven business regards the data it has and the data it can buy as the fuel that powers its success.
Identifying how data can do this is the first step. Once the business decision-makers are ready and eager to use data analytics, then start small with data you already collect. Build momentum until you’re combining data and generating powerful new insights. And finally take it to the next level by buying third-party data to really accelerate progress. If you’re not yet thinking about harnessing the power of data in your business, you should be. And the time to start? Today.