Why do businesses outsource analytics?
As the complexity of business data grows, enterprises are turning to third-party analytics providers for assistance. Here are ten reasons why businesses choose to outsource analytics.
There are numerous reasons why an organization might outsource the analysis it has already collected. Companies frequently collaborate with third-party providers to accelerate and refine their analytics insights and connect these insights to action.
Amaresh Tripathy, global business leader of analytics at Genpact, stated that the COVID-19 challenges have significantly increased demand for analytics outsourcing.
“We are increasingly seeing such relationships become strategic, where partners provide insights and participate in enabling action as a result of those insights through digital tools and change management activities,” Tripathy said.
This is frequently used as a center of expertise model, in which the partnership brings together a cross-functional team of business and technical experts and industry accelerators. Service providers bring digital tools, collaborations with other technology vendors, and greater access to third-party data to drive speed and sophistication.
Enterprises are also dealing with an increase in the volume and variety of data types.
“Outsourcing analytics not only helps to reduce the cost of analytics but also increases the value and speed of analytics,” said Sameer Dixit, Persistent Systems’ general manager of data, analytics, and AI/ML.
What is analytics outsourcing?
As businesses rely more on analytics, they frequently lack the resources to conduct these analyses. According to Benjamin Taub, CEO of Dataspace, outsourced analytics typically addresses two major shortcomings:
- A lack of expertise in approaches and technologies
- A lack of hands to gather and analyze data.
Taub frequently sees outsourcing teams brought in as experts to consult on and manage projects, as supplemental staffing, or as specialty firms with in-depth knowledge of a specific business or analytics technique.
Historically, the banking, financial services, and insurance industries drove analytics outsourcing to implement risk and fraud analytics, according to Alex Bekker, head of ScienceSoft’s data analytics. However, he now sees analytics outsourcing expanding across more industries, particularly healthcare and retail, with the most popular areas of interest being predictive and prescriptive data analysis.
“By outsourcing these advanced analytics types, businesses receive ML and AI-driven recommendations and forecasts for the next optimal step in their business processes,” Bekker explained.
Read more: How to Data Analytics Outsourcing?
10 reasons businesses outsource analytics.
Analyzing new data types
According to Rahul Prasad, head of the data and analytics practice at Infostretch, there has been a significant increase in outsourcing firms with cross-domain knowledge for weaving new data types into analytics workflows. Finance, for example, is outsourcing the use of unstructured data in analytics, such as news feeds and market research data. To improve video analysis, insurance companies are outsourcing domain expertise. Retail is outsourcing analytics to improve hyper-personalization, supply chain, and inventory optimization.
Improving forecast precision.
Tripathy said that the main reasons businesses work with service providers are to get faster, smarter access to their data and more business value. Some companies are forming outsourcing relationships in which compensation is tied to business outcomes, such as increased accuracy. For example, Genpact has a few strategic partnerships with CPG companies. It has promised to get better at forecasting by x% or cut inventory by y%, which clients can use to share or transfer risk.
When Ben Schein, vice president of data curiosity at Domo, a BI platform, led a data science team at Target, he delegated many manuals or less advanced data preparation tasks to a group in Bangalore. As basic tasks become easier to automate, he thinks there will be a shift toward outsourcing more complex skills, like developing algorithms.
These relationships should ensure that the outsourcing partner is not constructing a black box. The model must be alive even if the creation or maintenance is outsourced.
“I don’t want a static algorithm if I want to create an algorithm for the likelihood of repeat purchase. I’d like to be able to configure the algorithm and, if possible, own the code “Schein stated.
Making up for the lack of internal expertise.
Enterprises may also outsource analytics tasks to compensate for the lack of internal knowledge and people to handle implementation and testing. “Analytics outsourcing, like traditional IT outsourcing, allows you to get a lot more done in a shorter period of time,” said Taub.
The key is to understand what you truly require. An outsourcing partner can help teams understand the subtle differences between data scientists and Python programmers who are familiar with some data science libraries.
Obtaining domain knowledge.
According to Charles Miglietti, CEO and co-founder of Toucan Toco, a BI platform, “outsourcing provides enterprises with access to data analytics expertise when needed and allows them to quickly find specialists with up-to-date skill sets in the specific areas required.” This can be done by knowing how to evaluate, choose, and keep up with the analytics technology stack. It may also include business aspects. For instance, pharmaceutical and consumer goods experts can help solve data collection problems that are hard to handle and require agreements that cover large distribution networks.
Miglietti said, “If you choose a partner with domain expertise in your space, you can also expect to learn about best practices in your industry and powerful new ways to use data and benchmark metrics to guide your business strategies.”
Using breakthrough technology.
Enterprises frequently outsource analytics when implementing a breakthrough technology far beyond their current skill levels. “These types of projects are more difficult to execute, but they also provide new sources of revenue and unique differentiation to customers,” said David Tareen, SAS Institute’s director of AI and analytics. His team, for example, worked with a utility that wanted to use drones and computer vision to watch underground heat pipes for leaks and plan repairs. To do this, heat-detecting cameras had to be put on drones and stream video data to a deep-learning model trained to find small leaks.
Enhancing data storytelling.
Analytics outsourcing can help improve “data storytelling,” a way of interpreting and analyzing data through stories that makes business communication more persuasive and memorable.
“As a rule, people request analytics outsourcing when they have massive amounts of data, but they can’t read it and can’t leverage it to the full potential,” said Ivan Kot, director of customer acquisition at Itransition Group.
“At the same time, they create all kinds of narratives, like brand visions or monthly plans, and in most casescases, these narratives aren’t connected with data.”
For instance, operational managers may think their job is improving metrics like customer acquisition or lifetime value. Team leaders may wish to improve the KPIs of active managers. CEOs may want to start a new business, whereas founders may want to create hundreds of jobs in their home countries.
CEOs may want to start a new business, whereas founders may want to create hundreds of jobs in their home countries.
“All the narratives are related, but almost nobody has a bigger picture and can’t understand how their efforts contribute to the common mission,” Kot said. A BI consultant can combine all the existing stories and connect the dots between the different data views to make a story that everyone can follow.
Accelerating the time to market.
Mike O’Malley, the senior vice president of SenecaGlobal, says that outsourcing companies can also help companies integrate analytics across new acquisitions. Ability Networks, a company that makes healthcare technology, and his company worked together to quickly improve and expand their clinical and administrative cloud workflow products for hospital connectivity and analytics. This made it easy for Ability to swiftly add technology from a few key acquisitions to its main platform, which helped it get to market much faster.
Outsourcing firms can also assist in prioritizing critical analytics to improve specific types of decision-making processes, according to Dixit. His company has seen a big rise in analytics outsourcing in business functions like sales, marketing, support, and security. This helps prioritize outliers that are important to specific business outcomes. His team helped one customer look at support ticket data to make the self-service process easier for customers. This has resulted in a significant decrease in support ticket volume, saving millions of dollars. This has led to a big drop in the number of support tickets, which has saved millions of dollars.
Balancing skill sets.
Even though many businesses have spent money on consolidating their data, the art and science of finding meaningful insights require a mix of new skills like understanding the domain, statistics, technology, and storytelling. Many companies find it hard to hire and keep people with highly specialized and in-demand skills.
Sandhya Balakrishnan, the head of data analytics and engineering for Brillio’s region, said, “Outsource analytics shifts the burden of bringing together these different skills to analytics firms that train, groom, and mentor these in-demand skills.” Sandhya Balakrishnan, the head of data analytics and engineering for Brillio’s region, said, “Outsourcing analytics shifts the burden of bringing together these different skills to analytics firms that train, groom, and mentor these in-demand skills.”
While outsourcing is not always the best option, using it in data analytics makes it easier to distribute workload, provide different perspectives on models, and reduce bottlenecks while frequently lowering costs. As a result, outsourcing data analytics functions are becoming more prevalent at all levels.
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