In a society where data runs at the core of all we do, it is unsurprising that data analysts and scientists are increasingly depended upon. Big data is always growing — and whether that relates to revenue, client numbers, or products delivered, there is always a need for business owners to break down their numbers.
Small to large business owners worldwide call on data analysts to help them not only understand their numbers and their most important metrics but also track performance and improve service quality for the years ahead. Although business owners can measure their data, hiring a professional analyst to cut through it on their behalf is often more cost-effective.
Let’s look at how a data analyst can help a business in any industry make the most of its data to improve its standards for the future.
Focusing on key performance indicators and metrics
Data analysts are working in the dark without measuring key performance indicators (KPIs) and set metrics. A business owner needs to ensure an analyst knows what success looks like, and they can do this by setting clear metrics and KPIs to work with.
Students getting into big data through an online data analytics MBA — at Walsh University, for example — will learn early on that data is only so useful when one knows how to measure it and what to measure it against. In fact, Walsh’s program supports students who want to learn how to pull useful data and how to transform it into useful business insights.
For example, a simple KPI for a business might be to exceed a specific amount of revenue in a given trading period. An analyst would therefore track customer money paid into business accounts over, say, a year.
It is up to the analyst and the business owner to decide which KPIs define quality and success. For instance, revenue might not be such a driver. Business owners might want analysts to look closely at feedback scores relating to customer satisfaction surveys, and how many products returned to HQ during a year.
An analyst will work with a business owner to build a clear KPI plan so they can report back regularly on whether they are hitting their metrics. If they are not, business owners can then take specific action relating to the data an analyst pulls and investigates. An analyst could, for example, show that a business’s products return to HQ regularly and the volume is increasing, but that feedback remains unchanged.
That would suggest products that the company supplies are not necessarily broken or harmful, but might not be meeting all customers’ needs. It is up to the analysts to make these suggestions on the back of the data they tap into.
Using predictive analytics
With big data, ETL tools, and the right methods to crunch it, analysts can effectively predict a company’s future should they choose to continue moving in a specific direction.
For example, a business owner might turn to an analyst and ask them to show what might happen if they were to increase their prices for a specific product line temporarily. The analyst would then dive deep into precedent data, information available from rivals in the industry who might have tried similar tactics, and wider market research. By pooling together these separate pieces of information, analysts can build a picture of what a price hike might look like for a company on multiple levels.
Using data forecasting tools, analysts could show business owners that a temporary hike might lower customer revenue in the short term, but in the long run, they could see increasing returns as rivals in the industry struggle to keep up with their product quality.
Analysts need to work with various systems and use techniques they learn both in higher education and on the job to ensure accuracy. Of course, no one can truly tell the future — but skilled analysts using the right data and the best tools can create impressive forecasts that businesses can rely on.
Depending on the results of a predictive analysis, a company might decide to hold back on raising rates or stagger their pricing to weather potential storms ahead. Data analysts in this scenario would also look into overall market information and for any indication that the industry in question is set to experience a drop in interest or trust. In which case, they might suggest drastic measures in the form of reducing prices to keep people on-side while they weather what is to come.
Predictive analytics is just one side of the data analyst role — as forecasting often relies on preceding data, there is always a chance that things could be different this time around. In this case, analysts must pool as much data as possible and deliver confident reports to business owners.
Running predictive analytics in the aforementioned scenario can help businesses understand whether or not their product quality is considered good value for their pricing.
Running benchmarking and comparative analysis
Competitive and comparative benchmarking revolves around analyzing industry rivals. In the loosest sense, a data analyst in this scenario compares how successful the company they are working for is, in line with other businesses in their industry.
Again, success can look different depending on the metrics a business owner chooses. For example, they might ask a data analyst to look closely at brand awareness through social media channels. Are rival companies getting more views and likes than their social output?
Data analysts will measure a series of different metrics when running benchmarking analyses. For instance, they might consider customer reviews, experience scores, how they engage with a brand online, and how much money they make over a year. All these metrics help to build toward an image of success in customers’ eyes. Data analysts track and compare all the above information so they can gain a clearer picture of how their company is competing.
This type of analysis proves handy for breaking down a business strategy into tangible quantities. Business strategists can put information analysts together and use it to inform their marketing efforts and quality assurance.
For example, if an analyst suggests rival companies are receiving more positive reviews in customer care than the analyzing firm, there is a need to focus on retraining staff. If rival products receive better reviews, then quality assurance needs to step up.
Analyzing root cause
Root cause analysis revolves around finding a specific reason for a problem occurring, or for a trend emerging. At its simplest, root cause analysis involves digging deep into what might be the main trigger for an issue arising.
For example, a business owner might request a root cause analysis to look into why revenue might have changed over the past six months. In this case, a data analyst would start looking into what changed dramatically for the company over the past year. Once a potential cause of a problem arises, a root analysis then involves developing potential solutions to fix the broader issue.
Should a data analyst find that the root cause of revenue dropping over a long period is a result of negative press, for instance, they might run separate checks to narrow down the scope of the problem. They might find that it is certain press regarding a specific product, or that pricing for a particular range of items needs to change. In this case, an analyst would produce a full report on the root cause, why it is causing the problems, and how a business can effectively turn things around to avoid future downturns.
The job of an analyst in this scenario is to look at the bigger picture. They might find the root cause and a potential solution, but it is just as important for the analyst to deliver the news to business owners and strategists in a way that is easy to understand and immediately actionable.
Measuring risk management and compliance
Risk management, in business, revolves around whether a bold decision might be detrimental to the success of a company in the long term. In many ways, measuring risk ties in with predictive analysis.
A data analyst might look into information relating to previous customer behaviour, patterns evident in rivals’ strategies, and market trends, to decide whether or not it is worth taking the risk to close down certain product lines or change pricing. At the same time, a business data analyst can help companies understand whether their potential decisions will likely boost revenue.
Beyond corporate risk management in this sense, data analysts can also help business owners and strategists prepare for potential compliance issues. Regardless of industry, companies have compliance expectations — which keep their services legal and above board.
For example, a company producing cleaning products must observe certain safety laws. They will need to ensure their products are fit for purpose and work as sold, and they also need to provide safety labels so people can use them regularly while understanding the risks.
Failure to produce these labels would be considered a compliance risk. A data analyst would be under a company’s employ to review compliance legislation, data precedents, and rival information to ascertain whether a business decision is likely to break any laws or regulations.
Data analysts can produce reports that consider whether potential projects are likely to, for example, prove safe for public consumption. This type of analysis ensures companies not only stay on the right side of the law but also keep them trading — a strong reputation ensures continued revenue. It pays to dig deep into data.
Business owners will employ data analysts to protect their interests as much as the public’s — meaning this type of risk analysis ultimately benefits everyone.
Optimizing processes
Although they might not have a physical part to play in the production and delivery of services, data analysts can help business owners and strategists find new ways to become more efficient and produce better value products for their customers.
Data analysts can look deeply into demographic buying information, for example, to help shape future marketing campaigns. Consider a product that appears to be selling well for women aged between 30 and 45 — but markets toward women aged 20–30. An analyst might suggest that marketing tweak its approach slightly so that it drives more revenue from the real target market.
Analysts can also help prevent potential revenue and reputation loss by researching common customer complaints. They might find that a company received great reviews for product A but less so for product B. This information tells us there needs to be something of an overhaul to how product B is produced.
An analyst could look into what is most important for customers, too — is it pricing, features, or speed of product delivery? By measuring online mentions and social media data, analysts can produce a clear picture of what buyers think and therefore help companies to tighten up their processes.
Is data analysis crucial in the modern age?
Data is never going to stop growing — meaning there is always going to be the need to measure it so we can understand customer behaviours more effectively. The most successful businesses are leaning into data-driven decision making — it is more than just a passing trend.
For businesses of all sizes, hiring data analysts ensures there is always professional help on hand to break down complex information into actionable reports. With analysts’ assistance, business strategists can change their marketing and production processes to support customers better.
In turn, more support for customers frequently means a revenue boost — which is all the more reason to hire talented, qualified data analysts. Successful businesses will confidently say investing in this talent more than pays for itself in the long run.
For data analysts, there will always be roles available, meaning graduating in a data analysis discipline can open up plenty of opportunities and long-term security.