Data analytics is money in the 21st century. With the price of data collection, storage, and analysis dropping consistently — data is rising in value. We produce and capture trillions of gigabytes of data every year.
Forbes estimates that by 2025 we will have gathered more than 150 zettabytes, or 150 trillion gigabytes to be analyzed. Every industry utilizes big data, from entertainment to security. 94% of business enterprises state that data analytics are essential for growth and innovation.
Despite big data’s impressive investments, only 14% of enterprises make data analytics accessible to employees. While leadership wants to keep leading, the age of information hoarding is coming to a close. If companies want to take advantage of the minds that they employ, they will need to democratize data.
Data analytics helps teams and businesses extract value from the data they collected or bought. Data alone isn’t valuable because we cannot discern patterns, trends, or anomalies from it.
Let’s uncover how your company can leverage data analytics for better decision making.
Enterprises collect tons of data. Most of it will never see the light of day, let alone lead to decision making. That’s because we collect more data than we know how to process.
Reserving data analysis for executives and management is less profitable. Employees will have a weaker understanding of their own business and goals. Employees can also not make their own informed decisions, often delegating all decision-making to management.
The result is slow, opaque, and ill-informed decisions that front-line employees can’t understand. If you’ve ever tried to speak to an employee of another company only to hear, “I’m sorry we can’t do that … it’s policy” — you’ve spoken to a poorly managed company.
Employees should deeply understand why the company adopted a particular position. In doing so, they can better communicate, negotiate, and make better, faster decisions.
Besides data overload, due to the amount of data we posses, it can lead to flawed analysis and bad decisions. Making poor decisions from data might be even worse than not using data at all.
Here are four key steps to begin leveraging data to increase operational efficiency:
Your business isn’t clear to you unless you collect some form of data. Step one for improving operational efficiency is gathering data. Each team will have a better understanding of which metrics are important to measure.
In smaller cases, this could mean collecting data by spreadsheets in an Excel file. This step is relatively straightforward and requires little investment.
Issues can arise with the quality of the data you are collecting. If misleading data points are collected at the beginning, the entire analysis operation is useless.
Human errors, data losses, misunderstandings, or other mishaps can produce a subpar record. This form of data is often shared via email or on servers, leading to more errors. The key is to establish consistent metrics on which you can measure individual team projects.
Companies often rush into technological investments when they learn the benefits of them. Business leaders seek the best and fastest analytics software. Without preliminary exploratory data, it's hard to develop priorities.
Businesses should form data governance frameworks and establish clear purposes for their data. Without it, their investments will likely have a disappointing ROI.
Companies are best positioned by starting with their already-existing IT infrastructure. Next, link existing networks with external sources, such as IoT sensors or social metrics.
Once they establish a flow of initial insights spread across individual teams, they are scalable. Setting up sources for autonomous data collection is vital to do early on.
The processes and data streams that emerge from this automation are where businesses should invest. By putting money where the data already flows, companies immediately increase efficiency. If companies blindly invest in technology without a need for it, they'll run low on cash where it matters.
Additionally, these streams enable real-time feedback. Data analysis needs to be founded in operational efficiency, not the other way around.
Businesses are not modular; they should function as networks. In that way, the information should flow in all directions. Data-driven organizations (as a whole) allow decision-makers to combine data from different areas in the business to gain a holistic picture.
Sometimes individual teams are given more capabilities, tools, and data analysis power than others. This is most often because management sees specific departments as less likely candidates for data analysis.
Individual analytics programs are separated by divisions between different teams or departments in a business. Your marketing team will have separate analyses from your legal team — even though these projects are always somehow linked.
The process of data analytics data starts in individual departments. Often certain departments will take priority over others at the beginning. The company may have specific areas that require work, and other teams are less urgent.
This step is necessary before approaching data at an enterprise level. Working from the bottom-up ensures that teams are not scrambling to comply with rules. If rules are set by management without team workflows in mind, the integration will be painful.
Siloed data lets individual teams form their own process of data analytics. Teams can use their expertise to find efficient and meaningful collection points or methods. The challenge at this stage is that teams cannot share data between them.
Individual teams must begin to prepare data analytics in a compatible way. Decision-makers cannot utilize different viewpoints without similar metrics, methods, or software capabilities.
Adopt a best practices approach that will help integrate data from different departments. Create visualizations that demonstrate possible scenarios. Each analytics program needs criteria for success, as well as milestones on the way.
Decisions in a business affect all areas. Reducing the budget for marketing spending will influence sales, finance, reporting, legal, etc. Data needs to be shared amongst the entire business.
Equally, security becomes more and more critical. This doesn’t mean limited access; it means managing access to limit leaks. Centralizing information and knowledge makes it vulnerable to exposure.
The challenge here is capping costs and focusing on priorities.
Statistical modeling, AI, machine learning, and forecasting allow employees to create different scenarios quickly. Software like Microsoft’s Power BI can help your business build time-based intelligence.
Once data is shared equally through your business, you can begin to use analysis to make decisions. Modeling, mapping, and forecasting tools will help you visualize your data and trends. Your data ceases to follow your decisions and begins to lead them. Data protection also becomes increasingly important.
The key to successful predictions and prescriptions is to create a close relationship between data management teams and business management teams. Mixing and integrating knowledge of the market and business strategies with data analysis and modeling is a game-changer.
Complexity is the only challenge at this stage. Tools, processes, and models become demanding and hard to handle. Oversight and security are the most important factors to focus on.
Data analytics need not be boring or useless. Early data analytics integration is key to making better, more informed decisions down the line. You can’t make better decisions on data that doesn’t exist, isn’t structured, or presented clearly.
Many companies are collecting data, and some are using data to make executive decisions. But very few companies are integrating data analytics at every stage in the business. Few companies are empowering their employees to execute on common goals using data.
If you’d like to find out more about how data analytics can help your business boom, contact us and Acxtron will help you get started fast!.