Gone are the days of gut feelings, when decision-making in businesses and beyond relied mainly on the intuition of upper management. These days instinct has given way to evidence, concrete facts take precedence over conjecture and speculation has been replaced by calculation.
This is the age of data and data-driven business strategies. Today, businesses rely on massive pools of readily available information while making crucial decisions.
As users journey across the online web, they create streams of data points. These data points are then picked up by companies that utilize this information to better curate their products, services, and even marketing strategies to better align themselves with the needs of their potential customers.
A data-driven approach helps businesses deliver a more personalized, real-time, and relevant experience to their customers.
Modern businesses utilize data for a host of activities including making accurate recommendations, understanding how users behave on their web platforms, identifying population segments that are more likely to buy their items, and even re-designing entire products to better suit the needs of their customers, and much more. This is why data has rightly been termed the new oil.
And if data is the new oil, data analytics is the process of refining that crude oil to obtain usable fuel. Data analysts extract meaning from raw data and condense oceans of information into insightful knowledge which can then be applied to meet a host of business goals.
But not everyone has been able to cash in on the data boom. Even in this day and age, there is no shortage of companies, especially small businesses, which continue to struggle with attaining meaningful and actionable knowledge from the mountains of information they have accumulated.
One reason behind this failure is an over-emphasis on obtaining data analytics tools instead of designing and implementing concerted data analytics strategies.
So, let’s talk about some of the most important pillars of an effective data analytics strategy. The techniques we are about to mention will help small businesses dig out ideas from the heaps of treasured data they are sitting on, instead of just fiddling around with data analytics tools.
But before we jump onto our main topic, there is a very important fact that must be pointed out. To execute a successful data analytics strategy businesses must ensure that they have access to a fast-paced broadband connection like Spectrum Internet.
Because after all, if you cannot go online, you cannot hope to make a data analytics strategy.
Now without further ado, let’s jump in.
Decision-Driven Data Analytics
These days most companies are generating and storing data much faster than they will ever be able to analyze it, overwhelming their data scientists with piles of unorganized and unusable information. The phenomenon has become so common that it has even given birth to a new buzzword, ‘data fatigue.’
To avoid this pitfall companies must first design a clear set of questions they want to answer through their data instead of engaging in directionless experimentation. In simple words, business leaders should define clear-cut agendas for their data teams.
Because defining key performance indicators (KPIs) and goal-based metrics is a successful data analytics strategy. After all, only those who ask the right questions, get the right answers.
Another important aspect of this approach is maintaining a hygienic and clean data set. Accumulating vast reserves of unclean data serves to confuse data analysts and makes the process of extracting meaningful insights more cumbersome.
So, instead of focusing on collecting more data, focus on collecting relevant data.
Limiting your data analytics strategy to obtaining PowerPoint spreadsheets, complicated data points, cryptic metrics, and mathematical calculations will only get you so far. There is no point in putting the effort, time, energy, and money into analyzing large chunks of data if you are unable to communicate your insights to those who need them the most.
Remember, business managers, like you, are not data scientists, so do not try to be one.
Appealing visualizations and engaging narrative-based reports help convey data insights more compellingly and humanly, so get your data scientists to convey their insights in a more comprehensible manner so that they are discernable to you and can be converted into actions properly.
Data Literacy and an Organizational Approach to Data Science
Data analysis should not be put on a pedestal and dealing with data should not be relegated to a few tech professionals on the top floor of your office building. A data-driven approach to business management requires organization-wide data literacy.
This implies that every decision-maker in your small business, including you, must have the ability to at least comprehend, examine and apply insights obtained through data to day-to-day operations.
This is important because data scientists are not business managers and cannot be expected to tell you how to run your business effectively, they can merely offer insights. It is up to you, your managers, and your supervisors to implement and enforce data-driven decisions. If you cannot comprehend reports, you cannot implement them and what use reports if they remain unimplemented?
Data-driven decision-making has the potential to help small businesses grow but only if they properly strategize how to use their data effectively and can leverage insights properly.
So, before hiring expensive experts or firms make sure that you understand and apply some of the techniques we have talked about in this write-up.