Data–Driven Decision–Making: Supercharge Productivity and Profitability
How Companies Use Data
Large tech companies such as Facebook, Netflix, and Google, have successfully used data-driven analytics to solve problems and increase efficiency. In the case of Facebook, it was found that auto-populating a message related to removing photos increased use of the request 40 percentage points. In another example, Netflix used data analytics to develop highly rated original content based on viewer preferences. Google also realized success in a data mining project that created successful training programs for managers. The result was improved outcomes including staff retention and satisfaction.
Even small businesses can harness the power of the data-driven decision-making. Each click on a company website, emails exchanged, and purchases made, continuously generates data. This data can be compiled and analyzed to learn how to best meet their clients' future needs. This improves productivity and customer satisfaction which are both essential for a profitable business.
An additional scenario of using data-driven decision-making can be found in manufacturing and industrial companies. These businesses can utilize interconnected smart machines, vehicles, and tools to generate valuable data. This data can furnish management with real-time information that can be used to seek ways to increase operational efficiency.
Retailers, as well, can benefit from a data-driven approach. There is the need to keep abreast of customer purchases and trends to ensure stores are properly stocked with popular items. This can be done through the collection of social media data, web search trends, and even weather forecasts.
The payoffs of using data-driven decision-making are substantial. According to a study conducted by professors at MIT, companies that used this process realized a 4% increase in productivity and 6% higher profits. This process clearly is an effective way of reducing costs while helping companies gain a competitive edge in their industries.
The Origin of Data-Driven Management
Using data for decision making is not a new thing. Companies do it since multiple decades. But the development of big data was accelerated by the introduction of Hadoop in 2005. This open-source operating system efficiently stores and processes vast amounts of data using distributed systems. The original goal of Hadoop was to index the World Wide Web. It was one of the systems from which analytic operations such as interactive dashboards and integrated, centralized stores of all data, called data warehouses, were developed. DataOps, a methodology for managing data and streamlining analytics, was used on a more widespread level in 2017. The volume of information began to explode at around this same time.
Fast-forward to today with powerful database solutions that can easily handle data storage and analytics. One of the more widely used big data applications, predictive analytics, uses historical data to detect patterns and forecast data. It is this application that enables the use of machine learning.
Thanks to the development of powerful processors and chips, in-memory processing, or streaming can make analytics even work in real-time.
Steps To A Successful Data-Driven Decision-Making System
What follows are the sequence of steps to make the most out of data collection and analysis to help in decision-making.
Identify Business Objectives and Share Ideas
An understanding of the organization's goals from the executive level down is an essential first step in this process. These should be specified in measurable statements. From increasing sales to attracting more website traffic, this can help determine which data need to be analyzed and streamlines the collection process. Additionally, this time should be used to communicate the intent and commitment to use data-driven decision-making. This is important to garnering confidence and support through the organization and laying the groundwork for its success.
Upon identifying objectives and problems to solve, relevant data must be collected and prepared. These can be in the form of website analytics, social listening tools, customer feedback, and your internal enterprise data. Consider the amount of data that will be needed, how long it will take to collect, and if it is reliable and comparable. It is also important to consider if real-world or experimental data, or a combination of both, will be used in the decision-making process.
Fill in Any Gaps
Often times, some data needed for decision-making may not be readily available. These gaps may occur because of different systems used by various departments and different level of quality. If this is the case, alternative data collection methods must be considered. A cost-benefit analysis would be helpful in this situation to help determine the most effective way to go about this task.
Clean and Organize DataThe process of preparing, or cleaning raw data is performed to remove inaccurate, incomplete, or even useless data. This step can be the most time-consuming for a data analyst using conventional methods. There are tools on the market like BiG EVAL Data Quality Management, that help conducting this process more efficient and less costly.
Presentation of Findings
Effectively delivering the results of the data analytics is the last, but most crucial step in this process. A clear presentation highlighting key points and use of interactive tools such as dashboards can greatly impact the audience's decision. Visualization of data can better influence senior management and other staff to approve the project. Charts, graphs, and maps are examples of such tools and can make trends and patterns easier to understand. We recommend you a Podcast of BI Brainz where Raphael Branger is talking about modern and efficient data visualization.
Working With Data Aversion
But what if not everyone is on board this approach to decision-making? This can be a commonly encountered situation for many reasons. Some people may have had negative experiences in the past with poor data analysis projects and are not trusting of the process itself. Or they are not trusting in the data itself, because they had experience with poor data quality and are aware about the risks bad data quality brings. As a result, it is up to managers and leaders to convince this group to not resist this process. This can be done by presenting data as a powerful tool and even educating them about the power of analytics. By also publicizing successful previous projects using this approach, any hesitations can also be alleviated.
Common Pitfalls In Data-Driven Decisions
The collection and analysis of vast amounts of data can be a monumental task where mistakes can occur during any stage. Failure to detect errors in a timely manner which can result in poor decisions is one such example. An automated data quality process is needed to ensure that data brings the most value into the decision making.
Other obstacles are deep-seated in human nature such as a reversion back to subjective ideas or a bias towards a particular outcome or idea. Another interference is that of being easily swayed by the opinions of others and the desire to conform. In these situations, employees must be guided and refocused to make sound decisions based on data rather than their gut feelings or peer pressure.
Creating a Data-Driven Organization
Becoming a data-driven company does not mean simply installing and using the right applications and tools. It requires the strategy of using data and analytics to arrive at intelligent decisions that are in line with the goals of the organization.
Businesses that have successfully accomplished establishing a data-driven culture have several aspects in common. These include trust and commitment. Additionally, they have been able to develop a mindset that prioritizes data over intuition. Including personnel with different experience levels and perspectives can remove biases and promote diversified solutions. Everyone in the company should be adequately trained to use and interpret data accurately.
And finally, they take charge of their information quality and data governance. Then even in the best organized company, data quality can break a data-driven decision-making process or harm the business because decisions are made on wrong information.
The Future of Data-Driven Decision-Making
In today's fast-paced world, it is essential that businesses use data as the foundation for every decision to stay one step ahead of the competition. Tech companies paved the way for using data-driven decision-making systems with great success. However, from health care to education, data analytics are being used to solve complex problems, generate more revenue, and predict future trends.
The current COVID-19 pandemic is demonstrating just how much we rely on data collection and analysis. Not a day goes by that news coverage and social media are reporting daily case results and death statistics. All this coverage is reinforcing the importance of the collection and validation of the enormous amounts of data. Data scientists process and collate results into easy-to-understand information that the public can understand and act accordingly to help contain the virus.
In conclusion, data provides a complete picture of how the organization is doing from both a productivity and efficiency standpoint. This quantitative approach to decision-making helps management pinpoint where to implement policies and make necessary changes to improve overall performance and increase profitability.
Results that come out of data processing and are used for decision making are only valuable, if the basic digital information is of high quality, and if the data processing technology works without any errors. That is why BiG EVAL offers a variety of software solutions to automate data quality validation and enhancement as well as test automation for your data-driven projects. Contact us for more information on how our products and services can assist you in your data-driven decision-making solutions.
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