Successful companies put tremendous effort in mid- to long-term strategic planning which provides a healthy sense of direction and aligns an organization’s vision. We tend to believe that the best case scenario will manifest itself but the reality is that events rarely unfurl the way we plan – from disruptive competition, a sliding consumer base, or other unexpected obstacles, strong decision-making requires flexibility, vigilance, open-mindedness, and above all else, reliance on data.
The Information Age has generated more data than ever before thus raising concerns of exploitation, fraud, and misuse. As digital platforms become the primary way consumers interface with businesses, the exposure of personal and financial data has become increasingly vulnerable. But there is still plenty of good to be done when data is used properly and proactively.
It is not only the data itself – it is what you do with it
“We are a data-driven organization.” This is a recurring statement nowadays. Many businesses generate, process and store a tremendous amount of data. Being data-driven means collecting and analyzing data and using those results to determine a course of action and designing hypotheses to be objective, testable, and implementable. Some of those hypotheses will come from playing with data sets in a heuristic manner, and also employing sophisticated tools to generate insights in a more systematic, and sometimes unforeseen, ways.
While you may not have immediate plans for all the data you collect, that doesn't mean that you shouldn’t collect and store as much data as you can – and as long as you can afford to. It will likely come in handy when it comes time for deeper analysis, evaluating new product lines, or even pivoting your strategy.
Using data for organizational decision-making
“If you don’t get this elementary, but mildly unnatural, mathematics of elementary probability into your repertoire, then you go through a long life like a one-legged man in an ass-kicking contest.”
-Charles T. Munger, billionaire & vice chairman of Berkshire Hathaway
In the now classic Thinking, Fast and Slow, Nobel Prize-winning economist, Daniel Kahneman, introduced two distinct modes of decision-making known as the behavioral economic concepts of System 1 and System 2. Simply put, System 1 is an automatic and often unconscious way of thinking. It is autonomous and efficient and often requires little energy, but is prone to biases and errors. This system lends itself to quick decision-making often based on little information. System 2 is a more controlled way of thinking that is followed by a rational approach based on data collection – this is the best strategy for organizational decision-making.
Take the daily life of a CEO who needs to make countless decisions a day. Instincts and gut-feelings can play a pivotal role, but most long-term strategic decisions require a significant amount of deliberating, and decision-makers should take the time to use their System 2 responses to its full capabilities – and doing that effectively requires data. A study by McKinsey revealed that organizations with complete and more advanced data capabilities perform much better than their industry peers. Talent, hard work, instincts, and dedication certainly help but all things being equal, effective use of data separates the crowd into two distinct tiers of performers.
How should your partners approach data?
Today’s digital landscape means greater reliance on software or SaaS than ever before. As businesses become intertwined with multiple third-party services, they also need to ensure their partners, vendors, and suppliers place just as much, if not a higher, value on accurate and accessible data. Businesses cannot function optimally with these blind spots, and companies selling their services are now emphasizing data and data accessibility as a primary selling point to digital and e-commerce businesses. Your third-party provider should offer customizable, transparent and robust data access that suits your specific business needs. For example, as a payment service provider, Checkout.com offers varied and customizable APIs like our Reconciliation API that allows organizations to pull granular data easily and in real-time, along with a detailed breakdown of your transactions.
You should also make sure that your partners offer, in part or whole, autonomous control over your data. For instance, Checkout.com allows self-serve data pulls already formatted for your needs, so businesses have greater convenience by being able to pull data when they want, how they want – saving them an enormous amount of time by avoiding requests, wait times and back-and-forth communications in order to get the data you need.
Your partners should also offer full transparency and make data accessible to you at any time by offering the right tools and sophisticated APIs so you can extract detailed and customized data reports that will ultimately help inform short and long-term business decisions.
The right data is better than more data
According to Alistair Croll & Benjamin Yoskovitz in Lean Analytics, a good metric is inherently comparative, understandable, easy to act on, good for optimizing based on conflicting factors, and has the ability to change behavior.
Different stages of your business will determine your decision-making and subsequently, the kind of data you will need to collect. But the fundamentals of metrics should remain the same. For example, during the growth phase of your business, deciding which products or features to build may be dependent on tailoring products to gain early-stage customers and will more likely be bespoke. But as your company grows, decision-making strategies will change as you find your niche and cadence.
Your product roadmap will also become clearer as your customer base grows which means your focus on products will mean being focused on data. For example, as a product-focused company, we make sure that we’re asking our customers the right questions and are constantly looking for ways to improve through feedback, customer surveys and market research. Make sure your partners are constantly looking to improve through various data collection strategies like holding roundtables, focus groups, and building prototypes for testing and iteration. This shows that your partner is not only using data to improve on their existing products but also building new features that will benefit your business.
Build your data “dream team”
Change needs to come from the top. Leading by example is paramount and the push for innovation and implementation have to be defined at the top. According to the same study by McKinsey, senior-leader involvement plays a critical role in the effectiveness of a company’s analytic efforts – and securing internal leadership for analytics projects is a key tactic to ensure cohesiveness from the top-down. Once the vision is established at the top, change will need to happen at all level – and a data-driven point of view needs to be incorporated into every business process of the company.
Only in specific cases (independent businesses, semi-independent business units, geographically-varied profiles or activities, distinct product players of a product ecosystem) can we see some exceptions to this rule. And even then, if there is no driver at the top, they risk compromising their long-term viability.
Business leaders lead analytics transformation across the organization
Delivery managers deliver data and analytics-driven insights and interface with end users
Data architects ensure quality and consistency of present and future data flows
Data engineers collect, structure, and analyze data
Data scientists develop statistical models and algorithms
Visualization analysts visualize data and build reports and dashboards
Workflow integrators build interactive decision-support tools and implement solutions
Analytics translators ensure analytics solve critical business problems.
Your analysis can only be as good as your underlying facts, and the result will directly impact the way business is done. Business, technology, and analytics are inevitably intertwined and the skills required for a proper execution are broad and will have significant overlaps.
Want to learn more about payments data with Checkout.com? Contact our payments specialists today for more information.