Five Reasons You Should Use a Third-Party Data Analytics Solution
In mid-2019, the biggest name in e-commerce announced a plan to retrain more than a quarter of its large workforce for the purpose of increasing data science and analytics skills across the company. Amazon intends to allocate more than $700 million towards training employees in various technology fields including cloud computing and machine learning, but a stronger focus will be on data analytics. There is a crucial reason Amazon believes that their operations will be augmented by having staff members who are skilled in data science: analytics is what this company does best, and founder Jeff Bezos is a man who is convinced that analytical information management is the key to gaining a competitive edge in the 21st century.
What Analytics Can Do For Business
In essence, analytics has become the beating heart of what used to be referred to as business intelligence. The two main reasons business owners, executives and managers should embrace data analytics are:
* To obtain clear insights across all facets of business.
* To gain competitive advantages.
A good way to understand how analytics can boost the bottom line of a business operation, consider the following: In 2019, the Brazilian government under the administration of President Jair Bolsonaro determined that data-driven solutions are best strategy to reduce fraud in its massive pensions and benefits program. In Brazil, the traditional methods of detecting and preventing benefits fraud is through general accounting principles and investigations, but the National Institute of Social Security has faced great difficulties in managing a system that is expected to serve the needs of more than 200 million people. This is a system that struggles under the weight of $2.4 billion worth of annual losses related to fraudulent benefits claims, and it is overdue for reform. With analytics, investigators will be able to spot patterns of fraudulent activity and focus on specific cases instead of having to comb through thousands of records each week; what they need is accurate insights that will guide them directly to where irregularities are taking place.
When implemented correctly, data science processes can help companies achieve the following:
* Understand more efficient ways to increase profits.
* Cost reduction.
* Optimized risk management.
A 2018 Bloomberg research survey indicated that more than 90 percent of American business owners and directors have heard of analytics data processes, and quite a few intend to incorporate them into their operations, but there tends to be a disconnect in terms of making sure that the data is accurate, consistent and actionable.
Analytics is all about identifying opportunities to constantly improve business processes. When companies start using analytics, they immediately begin to realize many aspects of business that had been previously overlooked; for example, customer segments that had been largely ignored, repetitive costs, inefficiencies, and easy marketing efforts that were not conducted. Legacy business intelligence methods are mostly good at identifying short-term trends and solutions; today’s analytics are based on computer models that can shine a light on roadblocks or opportunities that companies can encounter in the long run.
Outsourcing Your Analytics Needs
Of all the business processes that can be outsourced, data science is the one that makes the most sense. Investing in analytics makes sense, but not all companies will be able to handle this process as an in-house solution. Getting back to Amazon, you may remember that this was a company that for many years was not able to post profits on its financial statements, and one of the reasons was that it was investing heavily in building a robust data science infrastructure; nonetheless, ever since Amazon began posting profits, it has not stopped, and it has not intentions of reducing its data science investments because that is exactly what keeps it way ahead of the competition.
With all the above in mind, here are five reasons you should outsource your data-driven solutions:
Cost Advantages
The return on investment (ROI) that analytics can provide is considerable, but start-up costs can also be quite expensive for some companies. There is a good reason most analytics solutions are delivered by means of cloud computing platforms: in many cases, the server infrastructure needed for data science processes is not something that companies can afford or manage. Rebuilding an existing digital infrastructure can be costly, and it can also take time away from your daily business operations.
If you already run a lean company with a data infrastructure that is remotely hosted on a cloud computing data center, or if your productivity software suite resides in a cloud platform that you pay for on a subscription service, you are already ahead of the game in terms of cost reduction. This is the same rationale that you should apply to your decision of outsourcing analytics, and it is not limited to reducing hardware expenditures; there is also the matter of having to hire data scientists and keeping them on payroll. When you outsource analytics, you can also forego having to recruit skilled staff members who tend to demand high salaries. You do not have to start out ambitiously; in fact, many business development experts recommend that you take tentative and budget-friendly steps related to outsourcing because you can always scale accordingly.
Greater Flexibility From Data Analytics
While many business owners think about cost savings as the number one reason for outsourcing business processes, many others believe in the tangible benefit of flexibility. Busy enterprises that are already operating at capacity do not want to be bothered with adding an internal data science department that managers have to keep an eye on. You want to focus on executing the results proposed by your analytics platform without having to worry about managing the platform itself. Business process outsourcing (BPO) firms are meant to be flexible, and this is not an exception with those that provide analytics on a contractual basis.
Keep in mind that BPO companies offer services that can be tailored to the needs of your companies. Let’s say you run an regional e-commerce operation that delivers thousands of packages each day with a dedicated vehicle fleet; by installing sensors in your fleet vehicles, you can process the data output and determine the condition of different parts before they become a problem. In this case, you can outsource the analytics to a company that will take the data and convert it into a dashboard that fleet managers can use to keep an eye on the mechanical performance of the delivery vehicles. If this is the only analytics process you are interested in, you can negotiate with the BPO vendor instead of signing a contract that will give you more data than what you actually need.
Time is Money
Getting back to the ROI factor in the aforementioned cost advantage section, your company may not have a lot of time to implement a data science department that operates on premise. Here is important to note that analytics has increased competition and sparked a race to the top; what this means for business owners is that they do not have a lot of time to implement an in-house solution because their competitors may have already chosen an outsourcing solution. Data science is something that IT personnel can eventually grasp and practice, but it is not an easy topic to master. You really want your IT staff to focus on core functions instead of having to retrain them in complex data science processes.
Even if you think that hiring an internal data science expert will yield better results, there is still the matter of the time you will spend searching for the right employee. If the new hire is expected to build your new IT infrastructure and a complete data science department, chances are that this will take a substantial amount of time; meanwhile, your competitors have already pivoted in this regard and are managing Big Data sets, perhaps through a BPO solution. Time is always of the essence, and you certainly do not want your company to lag behind while others are executing on profitable insights that were obtained through analytics.
Productivity and Competitive Edge
Depending on your style of doing business, this could be the most important factor in your decision to outsource analytics. If you are the type of entrepreneur or business owner who enjoys crushing the competition, you need analytics to help you accomplish this reasonable ambition. As of 2019, you can still find companies that believe analytics is something they can live without; this flawed thought process is a golden opportunity for business owners who can sense that they can easily get ahead by implementing an analytics solution.
Dismissing the power of Big Data and the insights it can provide is a mistake that can be easily explained by looking into the absolute success of Amazon, a company that relies on algorithms that essentially make us return to company’s website or mobile app over and over. Amazon simply knows what we want, and it knows how to push our buttons, and this was all determined by highly sophisticated data science. Let’s not forget that Amazon is upping the ante with $700,000 worth of data science training for employees; we can clearly see that this e-commerce giant feels its competitors making data science moves, and it is ready to continue moving ahead of them.
In terms of productivity, analytics can do more than just provide insights about employee performance. A 2019 research study conducted by McKinsey & Company on 400 corporate clients found that marketing can also be significantly increased by means of analytics by as much as 20 percent; one of these clients was North American retail giant Target, which figured out a way to predict when female shoppers would become pregnant, thereby allowing e-commerce marketing managers to target these customers with adequate offers, discounts, coupons, newsletters, and more.
As far as internal productivity, analytics modeling can reveal causes of problems by delving into reports, providing a diagnosis, making predictions about the potential of problems repeating themselves in the future, and issuing recommendations about the best way to move forward. With the right data mining strategy, your company can develop mitigation and prevention models so that your internal processes can run smoothly and with minimal problems.
Data Accuracy and the Bottom Line
When your analytics are handled by a third-party BPO vendor, you get more than just convenience. There is a corporate learning curve to analytics, one that can be significantly steep in many cases; in the course of learning data science and trying to figure out the best approach for the company, you are bound to run into errors related to data efficiency. Inaccurate data acquisition can result in potential mistakes and setbacks, but these situations can be avoided by contracting the right BPO analytics provider. The difference is that the vendor already specializes in business intelligence and data science operations, which means that you get a solution that is entirely focused on a sole process.
You always want to leave specialized tasks such as a information analytics to those who already have expertise and a proven track record. Business owners who believe that analytics are limited to the website insights report you can get from platforms such as StatCounter or Google Analytics are often surprised to learn that real business intelligence can be a tedious, time-consuming process. In the case of healthcare analytics for American medical businesses, for example, compliance with the Health Information Privacy and Accountability Act (HIPAA) is something that it is better left to professionals because in-house implementation could get in the way of patient care.
The bottom line of outsourcing your analytics processes is that your company can get started right away and without having to make a substantial investment. As with other BPO solutions, you do not have to worry about relinquishing management control; the third-party provider will take care of the heavy lifting in terms of capturing data from hundreds of software sources, identifying trends, presenting actionable insights, and providing recommendations based on Big Data scenarios, business intelligence case studies, and computer modeling. It is up to you to take action upon these recommendations, which your competitors are more than likely already following.
When you are ready to select a BPO analytics provider, here are some questions that you and your management team will need to ask:
* How will my company’s data be protected? The answers to evaluate in this regard will be related to secure connections, encryption and safe storage of data. Compliance with HIPAA and the General Data Protection Rule of the European Union come to mind.
* What makes your third-party analytics service unique? How will my managers be able to visualize data? In general, you should be looking for dashboards that display real-time information in a visual format that is easy to understand.
* What makes you the best BPO analytics option? Will you be able to deliver solutions that have a positive impact on our productivity? The answers you want to hear should hint at the level of flexibility the vendor is willing to provide, and it should be reflected on the contract.