The growing business scene means that our gut instinct is no longer enough to remain abreast of the competition. The dynamics of decision making have changed today as organizations are slowly realizing that data lies at the heart of decision making, meaning that they are becoming data driven decisions.
The technology that we use today is the underlying aspect of almost all businesses, and since technological means are interconnected, it has now become easier to collect data. Easy access to critical information means that enterprises can now observe the profile of the target market, helping them in predicting future behavior.
Initially, businesses did not have such apps and techniques at their disposal, as companies used conventional methods and past experiences to make decisions.
The evolution of big data paved the way for businesses to take a more analytical approach in decision making. The tool provides businesses of today with the power of information, a skill that is instrumental to the success of a business.
How Data Improves Decision Making
The outreach of the digital world today means that there are multiple channels for businesses to explore. In addition to collecting data through conventional means, companies also have access to mobile devices, social media pages, and other active data sources. A business today can collect relevant information from almost any source and then make business decisions based on that data.
The accessibility of real-time data ensures that companies of today provide better and improved customer engagement. The many channels available make it easier for companies to offer personalized services.
Companies can also use the information to enhance business efficiency. Successful enterprises of today use big data analytics to plan optimized selling strategies that eventually improve their performance.
The availability of critical data ensures that a business does not require extra investment with increased capacity. This not only provides an improvement in decision-making, but better delivery is also made possible.
The essence of making decisions based on data lies in consistency and continual growth. The data enables companies to create new business opportunities to generate more revenue, predict future trends, and optimize current operational efforts.
Now that you are aware of the ways data aids in decision making, here is a brief explanation of what data-driven decision making actually is.
What is Data-Driven Decision Making
Data-driven decision making, as explained above, is a means of working towards business objectives by leveraging available data, rather than just shooting in the dark.
The process of data-driven decision making starts with the collection of data based on measurable goals or KPIs. The sharp mind of an able entrepreneur analyzes the patterns from these insights and uses them to develop strategies that benefit the business.
Accuracy and relevance are the keys to collecting factual data. Incorrect or irrelevant information can cause you to make decisions that yield little or no results. This is why collection, extracting, and formatting is critical steps that help in obtaining accurate data.
Fortunately for enterprises today, the democratization and the development of business intelligence software empowers users to analyze as well as extract data. This constant need for data and its importance in decision making has led to the creation of a new field by the name of data science.
The goal of this relatively new field is to sift through massive amounts of raw data to make intelligent data-driven business decisions. The results of data mining come in two distinctive styles, qualitative and quantitative, both critical to making a data-driven decision.
Qualitative data relies on observation rather than measurement, while the opposite holds for quantitative data. However, it is the analysis of the collected data rather than the accumulation that is the end goal of the exercise.
Data-driven decision making makes or breaks companies at the end of the day. This exemplifies the significance of online data visualization in decision making. Companies that approach decision making collaboratively tend to better than those who use ambiguous methods.
How Data Analysis Helps in Decision Making
Since it is quite challenging to understand the steps and processes of big data analysis, we have broken down the complete process in the steps below.
1. Goal Identification
The first and most common step of big data analytics is goal identification. Organizations plan and identify their goals while taking related decisions accordingly.
The basic rules of goal-setting revolve around the critical principle of SMART. Experts believe that business objectives should be Specific, Measurable, Attainable, Relevant, and Time based. Even the data analytics techniques used to establish these goals follow the same steps.
2. Creation and Improvisation
Once you have established the goals, the next step is the creation or improvement of performance metrics that are used to reach organizational goals. Doing so can help a company in avoiding all non-related or insignificant data collection and analysis.
The elimination of non-related data makes it easier for a company to focus its goal as per customer requirements. This ensures that better analysis can take place, and goal optimization is performed easily and quickly.
3. Data Collection
This is one of the significant steps for all big data analytics. As mentioned before, data collection can take place from more than one source, helping the company to structure and un-structure itself accordingly.
Sufficient data from customers and or other peripheral sources aid a business in anticipating customer behavior.
The key point to data collection is the variable nature of data sources. The connectivity of technological means today prevents even a mouse click from going unnoticed. The variable sources can indicate a different target market altogether, for example, a notification through an Instagram handle or an email through a newspaper advert suggests different age groups.
4. Data Refinement
All collected data don’t need to be of your use. In this case, data cleansing is a must. The cleanup of data is usually in line with the objectives set. Deleting irrelevant data should be your top priority, as the professionals analyzing the results don’t need them.
Deleting irrelevant data is also vital because useless data can create confusion and lead to meaningless results. Categorizing information is also crucial to avoiding meaningless results, as it will help you in reaching your goals.
There are a lot of modern tools and software’s that help in analyzing or categorizing data. However, you need to choose the one that suits the needs of the business the best. Determine the type and requirement for your business and choose the tool accordingly.
Once you identify, categorize, and refine the collected data, it becomes essential to choose the appropriate tool to help you in data identification. Various statistical and analytical tools help you in analyzing data; however, all depends on the model and analytical methods you choose.
Hiring professionals is the best method for tool selecting and choosing business goals. The professionals will ensure that tool selection depends directly on business goals and is suitable for data at hand.
6. Process Execution
The last and the most critical stage of making data-driven decisions is the process execution. This stage is where it all comes to an end with a decision that reflects the data collected and is in line with the analysis of the experts.
The organizational goals are met only and only if the data processing is done correctly. Businesses today follow the steps regularly to enhance their performance and to look for new ideas to satisfy their customers.
Now that you are aware of the necessary steps of ensuring a more data-driven environment, the following tips and takeaways will enhance the data-driven culture in your environment.
1. Define Clear Objectives
The first step is to define clear objectives for a business. Collecting data before defining clear goals is a mistake that companies often commit. Rather than following the hype or whatever’s in business should set based on its Key Performance Indicators (KPI).
Although there are many indicators that you can choose from, you should avoid over-complicating things by concentrating only on the most important ones in the industry.
2. Don’t be Scared of Revisiting and Reevaluating
Often our brains skip to conclusions and are reluctant to consider the alternatives. Many businesses that fail blame their failure on the inability to revisiting their first assessments.
Verifying data and ensuring that you are tracking the right directions will help you in stepping out of your decision patterns. It is also important to rely on team members for a perspective and share it with them so they can help you see the biases.
Modern businesses are impatient and usually, don’t have a fallback strategy. This is mainly because most businesses today are afraid of stepping back and rethinking their decisions. It may feel like a temporary loss, but it is a necessary step to take if you wish for success.
3. Presentation of Data
As much as it sounds like your professor, the presentation of data is actually essential. Digging and gleaning looking for insight is nice, but more important is to convey the discoveries made in a meaningful manner.
An outline presented on a financial dashboard will ensure an at-a-glance overview of the financial performance of a company. The top KPIs such as operating expenses ratio, net profit margin, and earnings before interests and taxes aid in a fast decision-making process.
With the availability of data visualization softwares, you don’t need to be an IT expert in decoding the financial performance of your company.
4. Don’t Over-Rely on Past Experiences
One of the things that can lead to the failure of any business is an over-reliance of past experiences. Looking behind you or focusing too much on the past can cause you to miss what is in front. Environments and markets change all the time, a formula that guarantees success today will become a possible method tomorrow.
Therefore, in light of the changing environment, managers need to combine past experiences with current data.
5. There is No Such Thing as Gut Feeling in Business
Some managers boast of following their instinct and the positive results it has led them to achieve. However, the thing they don’t mention is that they first trust the gut, and then follow it.
Following a plan of action, instinctively can turn out to be a recipe of disaster for a business. Business experts claim that the only line of action for business decision making should be research and analysis.
Becoming a data-driven organization is not a simple, walk-through process. Waking up one morning and then deciding to use data for decision making is not the way companies change their attitudes.
Change has to come from the top as it is the responsibility of the business hierarchy to change the company culture. The best way to get the leadership to sit up and take notice is by showing and proving how analytics bring value to an organization.
A revamp of business trends and styles is possible only through the workforce. A business needs to install the value of data in its workforce. As the employees start applying these insights directly in the business, using them in the decision-making process will embed within the organization.
Revamping the attitudes is also about realigning the culture of the organization while ensuring that each and everyone is aware of the benefits of using data.
While the business does need a professional for data mining, it does not require a data scientist to analyze it. Following the simple steps mentioned above will help in making your business decisions more data-driven.
Successful companies such as Google and Walmart are an example of the benefits of being data-driven as their success story is for all to see.
While we do not suggest blindly following whatever the two do, it is imperative to understand their method of success, which is data-driven decision making.