Big data became more than a buzzword when industries discovered its huge potential. And with this finding, came a new science called data analytics.
The demand to understand all the data we’ve been creating and amassing has grown to a level, which not all businesses among different industries can cope with—making outsourcing a valuable option.
But, is it really worth outsourcing data analytics? More so, is data analytics even worth investing on? Read on to find out.
I. What is Data Analytics?
Data analytics is the science of examining raw data using qualitative and quantitative techniques and processes for the purpose of drawing conclusions from these data. Many industries have been using data analytics to help companies make better business decisions.
It is different from data mining by way of scope, purpose, and focus. Data mining is done to identify undiscovered patterns and establish hidden relationships while analytics is for deriving conclusions based on what has been gathered from the data by the researcher. Data mining focuses on pattern or trend establishments, whereas data analytics focuses on inference.
While analytics has always been used in every industry, it is primarily concentrated among business-to-consumer (B2C) applications. It has long been established that studying evolving data facilitates better decision-making, and with how big data is making the waves these days, it is no wonder that businesses are taking advantage of it and using analytics to help them identify key user and customer trends for strategizing wisely.
II. Why Data Analytics is Important in Today’s Business
Companies have realized just how much and how valuable actionable insights they can derive from big data, with analysts at ABI Research predicting more than 30 billion devices being wirelessly connected by 2020.
According to PopYard, the digital universe will grow from 3.2 zettabytes today to 40 zettabytes in only six years, with 85% of that data coming from net-new data sources. And these sources, including mobile, social media, and web- and machine-generated data, present both a challenge and an opportunity for enterprises globally.
Big data drives software growth, which has a figurative and literal role in today’s industries driven by technology. The compound annual growth rate (CAGR) for the 2013-2018 worldwide software market will hover near 6%, research firm IDC predicts.
But, big data-related categories, including collaborative applications and data access, analysis and delivery solutions, and structured data management software will show a higher CAGR (around 9%) over that five-year period, says IDC.
As we can obviously infer, big data has a direct proportionate effect towards data analytics. The more data we collect, the more we need to analyze it.
So, how important is the role of data analytics in today’s business?
- Analysis of transactional data provides companies with actionable insights into their operations, helping them improve their working capital management, increase customer acquisition, retention, and satisfaction, among many other things.
Data analytics can truly improve the human experience. It’s not the big data that will transform lives but how you collect, filter, analyze, and interpret it that matters.
At present, most companies estimate they’re analyzing a mere 12% of the data they have, according to a recent study by Forrester Research.
A lack of analytics tools and “repressive” data silos are two reasons companies ignore a vast majority of their own data, says Forrester, as well as the simple fact that often it’s hard to know which information is valuable and which is best left ignored.
In companies, analytics can be implemented in processes to measure the right key process indicators. From there, they can create new tools or techniques to implement new models, algorithms, and statistics to identify weaknesses and internal growth opportunities. These can lead to better redesigning of procedures or certain operations to deliver improved business outcomes.
- Several industries can benefit from data analytics, mainly communications, digital media and e-commerce, retail, financial services, healthcare, etc.
Data analytics has moved from just being descriptive (e.g. reports and alerts) to predictive, providing statistical analysis, predictive modeling, forecasting and optimization for anticipation, and planning for future scenarios.
Imagine how it can be used to manage healthcare, predict disease or outbreaks and prevent them, improve and act on homeland security, and give more needed products and services to the masses, etc.
The retail industry can easily identify opportunities for new products and strategize for profit based on market research trends that can now be done digitally—examining consumer behavior through social media and buying experience and engagement without necessarily going through traditional routes such as surveys and interviews.
It is obvious that data analytics is very business-driven. With the right infrastructure and execution (manpower and executive implementation), it can have a powerful impact on customer engagement, frontline units of almost every industry and operations.
Speed plays a big role in how important analytics is as well since innovation these days is critical. Identifying and implementing the right solutions in any business spell the difference between its growth and failure.
III. Challenges of In-House Data Analytics
Having the capability to process and analyze big data has proven beneficial to companies. Companies with superior data analytics such as FedEx Corp and Wal-Mart Stores Inc. have found ways to build long-term advantages, as they have a competitive edge over their competitors in their respective industries.
The surge of big data in the new millennium has led to a demand for teams that can analyze it. Hence, the growing demand for data analytics teams or scientists. However, keeping an in-house analytics team can be difficult, as:
- Most companies lack the knowledge and experience needed to put together a team.
Since big data and the industry that sprouted from it is fairly new, most companies don’t have a clue how to start collecting, using, filtering, and more so, evaluating big data.
Since it also cuts across through several industries, even those that have not relied on technology for so long, the experience needed to train a team is lacking.
- The market for analytics specialists is increasingly tight.
Fortune recently reported, “Online help-wanted ads for data analysis mavens have shot up 46% in April 2011, and 246% since April 2009, to over 31,000 openings now,” according to job-market trackers. In fact, Harvard Business Review called this profession the sexiest job of the 21st century.
This is actually the driving force why companies are considering outsourcing their data analytics. The shortage of those that are capable of developing and leading world-class teams that can enable a company to create a competitive advantage from its data and analytics is notable, and in fact, drives another challenge;
- Having a dedicated team of data analyst experts will require more financial expenditure.
Increased demand and a shortage of it would directly increase data specialists’ asking salaries, and most companies aren’t ready to invest that much on manpower, in addition to spending on the required infrastructure (equipment) to run an in-house analytics team.
According to Modis, a global IT staffing services provider, data scientists remain in “high demand but short supply,” which translates into generous six-figure salaries for some PhDs with relevant big data experience.
Company executives and business owners know that they need to rethink their sourcing strategies and fragmented technology operations and departments to meet demand for a faster, better, and less expensive innovation around analytics. Outsourcing is the best solution.
IV. Why Data Analytics is Better Outsourced
If you have any qualms about outsourcing data analytics, statistics don’t lie, and it says that outsourcing is the trend of the future.
TechNavio’s analysts forecast the Global Data Analytics Outsourcing market to grow at a CAGR of 31.68 percent over the period 2012- 2016. One of the key factors contributing to this market growth is the rapid expansion of data.
With how much big data is being collected daily, there’s a noticeable shift from transaction processing to analytics, and insight-driven action among companies today. In fact, data-driven business decisions are not seen as a nice-to-have, rather a need-to-have capability in most companies today.
Data is driving demand for both greater business insight and the foundation to deliver it. Unfortunately, most companies struggle to meet this demand. And this is why outsourcing becomes a valuable option:
- Innovation is hugely time-driven. Implementing innovation requires the right infrastructure, skill set (professionals), and mindset to get it done—on time.
With so much at stake, data analytics needs to be done right and on time. Most in-house enterprise IT teams can’t handle the pressure, as this isn’t exactly their area of expertise (which most company heads mistakenly assume).
Most IT departments are being asked to step up to the need to grow the business by enabling new analytics into the company besides building infrastructure that can house big data, without causing a huge drain in their budgets.
Accenture’s research found that 48 percent of high performers identify ways to use data and information from the services to capture additional benefits, versus only 25 percent of typical performers. Because insights gleaned from analytics can deliver real outcomes, Accenture has seen leading clients re-align their business models to take advantage of this new source of value creation.
This is driving a whole new generation of the market, where innovative providers are leveraging large volumes of data, combining analytical tools and technologies with industry/functional knowledge to create business insights.
Most companies want solutions that deliver a quick ROI, can be implemented quickly and affordably, without a huge drain on their current IT setup. This is close to impossible, making outsourcing a better option. This will help you achieve your goals faster than creating an in-house team—scouting for talent, setting up tools from scratch, etc.
- Outsourcing data analytics offers more flexibility.
Time-sensitive projects or those that require one-off reporting and analysis are better off outsourced.
- Outsourcing data analytics allows you to be free of unplanned stressors on a daily basis when it comes to overseeing in-house operations.
Keeping operations in-house may give you the idea of having more control of the big picture. But, you’ll eventually be distracted by overseeing your in-house team operations, monitoring their progress, or even seeing unplanned downtimes or hurdles.
This eventually boils down to you not having more control of the big picture but actually taking your focus away from your core businesses.
- Outsourcing data analytics allows you to have global, world-class experts using state of the art and always updated tools.
Outsourcing providers offer services that they have worked hard to learn, specialize, and excel in. Since it is their core business, the right providers keep their service at its finest, employing only the most capable data analysts, using top of the line equipment, ensuring they meet global standards.
The best outsourcing partners even innovate and set benchmarks in the industry.
- Third-party validation is a huge advantage.
If you want to look at your data from a fresh perspective, outsourcing data analytics can give you a refreshing viewpoint—one that might even surprise you.
When you’re used to looking at information at the same angles, it’s easy to miss all the wealth of information just by flipping the other side of the coin, or looking at it a different angle.
- Outsourcing providers are able to offer both horizontal and vertical solutions packaged in a variety of configurations.
Data analytics spans a broad range of services, techniques, toolsets, and vertical specific expertise, most often needing a combination of everything when doing projects.
Outsourcing vendors, because it’s their core business, have become more sophisticated as they handle large, complex datasets and projects. From data management, modeling, reporting, insights, tool design, and implementation and developing business rules—the right outsourcing partner can deliver.
As you can probably glean from the range of services mentioned, data analytics is not an isolated vertical business operation. Hence, its success can determine how well you can analyze other critical aspects of your business operations.
These six reasons are even grounded on data – recent Accenture research found that 42 percent of high-performing BPO relationships – those found to get the most business value – considered analytics provided by the service provider to be one of eight important components of the BPO relationship, compared to just 28 percent of typically performing BPO relationships. This clearly proves that outsourcing data analytics is going to be part of every industry’s strategic business decision.
V. Issues to Consider in Selecting Data Analytics Outsourcing Provider
Now that we have established that outsourcing data analytics is a better option, it’s also important to note that outsourcing is not a one-size-fits-all solution. While we all want the same thing—better, faster, cheaper analytics execution, finding the right partner who understands your needs can be a taxing task.
There are still several issues to consider when selecting a data analytics outsourcing provider now that you have decided to outsource. Here are some things to think about when making your selection:
- Identify which structural option and variable you want to focus or optimize.
Is it cost, quality, productivity, innovation, time to market, or a combination of everything? Also, decide on how much of your data analytics silos are you outsourcing. As mentioned, it is both a vertical and horizontal operation, and you need to study carefully which ones will be better off outsourced, or if you will keep some in-house.
A good way to define the scope of outsourcing data analytics is this—outsourcing data analytics can be divided broadly into the purpose for process improvement, real-time support, or future planning.
Here are some operations that fall into these categories:
Process improvement: Data, Content, Standard and Integrated Reports
Real Time Support: Alerts (how it is connected), Decision and Support (What is happening?)
Future Planning: Forecasting/Modeling, Optimization
Out of the three, process improvement is the majority of data outsourcing services offered today. Shortly, more providers are seen to offer more decision-based analysis reporting and forecasting/modeling.
- Find out the capability of the team.
Once you have identified the scope you want to outsource, the next thing to consider is the capability of the provider. Most firms and vendors are capable of report generation, descriptive statistics, or dashboard generation.
It is also important to note that analytics is very different from collection and processing. Analytics is inferential, and its reports generate insights. It is also important to know what analysis approach the vendor can give, or if they can do analysis based on your data requirements.
- Ensuring a productive relationship.
Outsourcing to a vendor means a productive partnership is met. In the MIT Sloan Management Review article, “Should You Outsource Analytics?,” Bell and his co-author, business analytics expert David Fogarty, found that culture often proves a sticking point, especially if the outsourcer is inherently global (which most offshore outsourcers are) and the buyer isn’t.
Dealing with a global organization and global resources can be quite challenging for a company not used to operating in that sphere and on that scale, Fogarty said.
- Data security and protection.
Data is a huge asset (and liability) of a company. Its protection and privacy is of utmost importance.
Before entering an outsourcing agreement, Bells advises to understand what processes your selected partner is using to protect your data as intellectual property. This includes asking questions like “How are we going to protect our intellectual property?” (via encryption, for example) and asking the outsourcer, “How are you going to isolate the resources that you’re developing for us?”
- Exit or expansion, buying-in strategy.
When you decide to outsource, the decision to bring operations in-house as your company learns more about data analytics usually happens after a few years of productive partnership with your vendor.
You need to be up-front about your exit plans, stipulating terms for transition in your contract agreements. You also need to be truthful whether you are testing the waters before deciding to enter into a more comprehensive partnership.
Infinit Datum offers Data Analytics outsourcing services to businesses and individuals who are highly dependent on data in making business decisions. Our years of experience have exposed us to the latest industry trends and techniques, allowing us to deliver the best possible solutions to organizations across various fields and industries.
Every client is different from the rest. Hence, we customize our services according to the unique requirements of each one. Combining our years of experience with the latest industry trends and techniques, we deliver the best possible Market Research solutions specific to your needs.
Contact us for more information on how we can help you.