How Big Data is Changing the Way Companies Manage Indirect Expenses
Last updated: October 27, 2017
Reducing indirect spending can save companies more than 25% on their overall expenses. Cutting down on unnecessary travel costs, utility spending, office supplies, lab supplies, professional services and much more can increase your bottom line significantly. Because of these savings, indirect spend management has gone from minute supply chain detail to crucial overall business strategy in recent years.
Indirect expenses include a wide variety of categories and span multiple departments, making it difficult to keep track of and analyze. Even companies that realize the importance of efficient indirect spend management can have difficulty implementing meaningful changes. This is because to effectively reduce indirect expenses, you need to have a thorough understanding of your spending patterns. You must know what you spend money on, how often you spend it, how to categorize each expense. Having access to this data allows you to gain valuable insights that you can use to make optimal changes.
All of that information amounts to a lot of data, which brings to mind a term that you may have been hearing a lot recently — big data. Big data is more than just a passing trend. It’s revolutionizing all types of industries and aspects of business, including managing indirect spend. Big data, related tools and the actionable insights they provide can help you to cut your indirect expenses and dramatically increase your profits.
Big data is a phrase that folks in the business and technology worlds have been throwing around a lot lately, but what exactly does it mean? The technology we use today produces massive amounts of data, far more than we have ever produced before. Pretty much everything that’s connected to the internet produces it — social media, websites, online financial transactions, smartphones and connected devices like smart thermostats.
We produce a ton of data, 2.5 Exabytes every day to be exact, which equals 250,000 Libraries of Congress or 150,000,000 iPhones. And the amount of existing data is quickly expanding. In 2013, it totaled 4.4 zettabytes and by 2020, is expected to reach 44 zettabytes.
The Internet of Things, which refers to the vast network of Internet-connected devices, is helping to speed up that growth. Smart refrigerators, light bulbs, cars, security systems and even toothbrushes are becoming more popular. Even infrastructure, like the electric grid and natural gas pipelines, can be connected to the Internet. The number of IoT devices will continue to grow in the coming years.
All of this data is useless, though, if you can’t translate it into something meaningful. This has spawned a burgeoning field of data analysis that uses machine learning and other advanced technologies to make heads and tails of this data. These data insights can be used to spot fraudulent activity in the banking industry, identify patterns in patients’ medical history in the healthcare industry and much, much more. One important use is, of course, optimizing business’ operations, reduces expenses and increasing profits.
Using big data to reduce indirect expenses is a perfect fit because much of the necessary data is already being collected and there’s enough of it that valuable insights can be gained. Most businesses have many different types of indirect expenses. Correctly identifying, monitoring and addressing these costs can significantly increase a business’ bottom line.
There are many challenges with indirect sourcing, but there are solutions as well:
- Office Supplies
- Office Technology
- Insurance and benefits
- Professional services
- Equipment maintenance
These areas have a lot of moving parts, which means they potentially have a lot of data to work with. This also means that tracking it can be challenging. Indirect costs typically come from a wide range of suppliers, even within the same category. Different people within the organization across different departments are also responsible for managing this spending. To reduce indirect expenses, you often need to convince multiple people to reduce their expenditures.
Big data helps with this because it allows you to track information automatically and analyze it automatically as well. Certain tools collect all relevant information in one place, enabling you to easily spot larger trends and keep everyone in your organization in the same place. Rather than having to manually manage a wide variety of data streams, you can automatically collect it and organize it through the use of technology.
No matter what industry you’re in, you can likely save money on your indirect expenses, and there’s a good chance that you produce data related to that spending. Finding that data, analyzing to find actionable insights and acting on those ideas can cut your costs and improve your bottom line significantly.
Data collection can take multiple different forms, from manual gathering to today’s technologically advanced solutions. As technology has progressed, it’s enabled companies to gather more and more detailed data. Although manually logging transactions and other useful data can be useful, in today’s world, businesses need to utilize technology to keep up with the competition.
As a business, you can collect data actively or passively. An example of active collection would be asking customers to fill out a form with their contact information or conducting a survey. Passive collection would involve using cookies to collect information on web browsing activity or the location-based information provided by mobile phones.
As it refers to indirect expenses, active collection might involve asking the heads of each department to submit spending reports. Passive collection would be more automatic. You might automate regular payments and store transaction information so that it’s always accessible. Your accounting software might connect to company credit cards and track spending as it happens.
Using software to track spending reduces the possibility of human error as well as foul play. You can’t miss recording a payment when your system does it for you automatically. You’ll also be able to avoid fraudulent charges and paying invoices for items you didn’t order. This automatic monitoring of financial data also makes sure that all of your data is available for you to analyze.
For data collection to be valuable, you need to be able to analyze it and uncover valuable insights. These insights are what allow you to make changes that save you money. To come up with these ideas, you need to look deep into the information you gathered.
Before you start analyzing your data, first decide what it is that you’re looking for. Defining your goals beforehand will help you to organize your information appropriately and look in the right places. Once you’ve done that, start looking for patterns. Try to spot anomalies and determine what influenced the outcomes you’ve discovered.
To do this, you can use a wide variety of analysis methods including A/B testing, logistic regression and association rule learning. You can do this mostly manually or employ advanced technology to help with this. Companies today often use algorithms, machine learning and more. Standard query language, or SQL, is the standard for communicating with a database today, but that may change in the future.
How you present your data and insights is also important. You want to display it in a way that people can easily understand and that will inspire them to take action. Visualization is particularly effective at this.
You can purchase software and other resources and complete your big data analysis, or you can hire another company to do this work for you. These kinds of companies offer what is sometimes called Big Data as a Service or BDaaS. This provides you with the power of that company’s resources and expertise without having to make a big initial investment or spend too much time on the subject.
They say knowledge is power, and big data analytics is a perfect example of this. Data and the insights you can gain from it provide you with a deeper understanding of your business, your customers, your employees and more. The potential for this endeavor is far-reaching. It could produce results related to almost anything you can imagine, given the right resources.
These data insights enable you to predict the future and learn from mistakes made in the past that you otherwise might not have even known about. Every day, businesses spend money on things they don’t really need or overpay for certain commodities. With the power of big data, you can change all of that.
General Electric produces a lot of data. Most of their machines, which operate in power plants, hospitals, factories and many other places, produce huge amounts of it. GE collects this data, analyzes it and then uses it to make its machinery operate more efficiently. The company estimates that data could increase the United States’ productivity by 1.5 percent and increase the average national income by 30 percent over 20 years through cost savings.
Another big data success story comes from UPS, which, as the company says, specializes in logistics. The company makes nearly 17 million deliveries every day, so it has a lot of data to work with. It began tracking the performance of its vehicles in relation to routes, idle time and maintenance and then analyzes the data using algorithms. The result was 39 million fewer gallons of fuel burned and 364 million driving miles avoided.
Big data analytics might bring to mind imagery machines humming and crunching numbers, and while that’s certainly part of the equation, there’s a human element too. Someone needs to be there to organize the data collection, determine what patterns to look for and, vitally, take action based on the resulting conclusions.
Some companies might outsource this work to a company that specializes in it, while others might prefer to hire an internal worker to do it or to learn how to do it themselves. Either way, the process requires a human to manage it. And the employees at your company need to implement the changes that your data work suggests.
If you plan on analyzing your own big data internally, look for someone with experience in computer programming or computer science, mathematics and statistics. It’s also helpful for this person to understand business and accounting for businesses. Big data analytics is a young field, however, so these requirements might change in the future.
If you want to boost your resume in the field of big data, you can pursue any number of certifications in the field. These programs will hopefully teach you something and help you get a job. Technology companies such as IBM, SAS, MongoDB offer a variety of certifications. Some of the tops ones include CCP Data Scientist, MongoDB Certified Developer, Oracle Business Intelligence and SAS Certified Data Scientist.
As mentioned earlier, big data is still a young field. It’s expected to grow incredibly quickly in the coming years and will likely change a lot. Getting started now, though, can help companies avoid having to play catch-up in the future.
As the field progresses, the volume of data will continue to increase. Wider varieties of data types will be generated and collected more quickly, and more organizations will have access to data. In the future, the vast majority of businesses will likely rely on data to run their businesses and drive growth.
Tools for analyzing data will also improve. They’ll become more automated and easier to use so that they’ll require less technological expertise. The business model of big data as service will likely continue to grow, as will use of the cloud and machine learning. We might have access to more real-time data insights as well.
Although the field and the related technology are still developing, it’s already impacting almost every industry. Getting involved now would be a worthwhile investment, especially since it can save you money through reducing indirect costs and optimizing other areas of business operation.
In the not-so-distant future, all aspects of a business will use data to improve their operations. Accounting will use it to track and analyze spending and income. Supply chain managers will be able to harness actionable insights from data to optimize procurement systems. Management will be able to increase profits both by improving performance and cutting costs, all thanks to insights gleaned from big data.
Even if you don’t use big data to identify cost savings, looking into your indirect expenses is still a smart and potentially lucrative endeavor for any business, especially since it typically comprises between 15 and 27 percent of total revenues. You might be surprised at the extent of the cost savings that you come across when you analyze your indirect expenses.
At Dryden Consulting, we’re experts at combing through companies’ spending patterns and identifying ways to make that spending more efficient and cut costs. Because of the wide range of spending that companies typically manage, this can be a complicated process, but we have the tools and skills to produce results.
We’ve worked with companies like Coca-Cola, Microsoft and Nestle to analyze and optimize their spending to improve their bottom line. We can do the same for you. If you want to find the hidden savings that exist in your daily operations, we can help you collect data on and analyze your indirect spending. We offer a variety of solutions that can help you save money and increase profits. If you want the experts on your side, contact us for indirect spend procurement consulting.