Once upon a time, when brands needed to test the performance of their product or campaign ideas, they would busy themselves organizing focus groups and conducting tedious market surveys to figure out if were on the right track. Subsequent business decisions would often be based on the inadequate results, past learnings and a strong gut feeling. While this approach probably simplified things to a certain extent, the results were not always reliable.
Fast forward to the 21st century and the scenario has changed significantly. In the connected digital ecosystem, companies have unfettered access to customer data across myriad channels – from social media channels and smart products like wear-ables, to mobile applications and search engines. Marketers can now aggregate information from various such sources and after cleaning it, structure it based on their requirements using big data technologies. This is ultimately fed to an analytics engine which translates the data into actionable insights. The result is focused, targeted and personalised marketing strategies and product launches that hit the bull’s eye more often than not.
Needless to say, without Big Data analytics implementation, the modern enterprise is all but blind, especially considering that they analyze a mere 12% of the information that is available to them.
Budgetary Constraints and Talent Gaps
Research has been unanimous on the value of transitioning to a Big Data analytics and Business Intelligence-based operating model, but companies are often slower to embrace this approach. Out of the 87% of enterprises that are trying to make the shift, only 37% report meeting with some measure of success. One of the impediments to the transformation process is a certain degree of cultural resistance, with 62% of executives still preferring real-world insights instead of depending on analytics.
Apart from the lack of support or confidence from the company’s decision-makers which reduces the chance of making future investments for analytics projects, there is a worrying skill gap of data scientists and data analysts, which further impedes enterprise-wide adoption. 39% of CIOs and IT professionals from across industries agree that data scientists are extremely difficult to find and recruit.
Subsequently, three in five C-suite executives fear that these factors will likely converge and lead them into obsolescence, as digital natives begin to push them out of their market leadership position.
Whether its customer analytics, marketing data analytics or business intelligence analytics, analytics projects are still seen as a part of a company’s IT initiatives. And they rarely find any dedicated allocation in the annual investment plans – more so because IT in itself eats up more than 70% of budgeted dollars. Moreover, rolling out the infrastructural changes necessary for supporting an analytics project while meeting regulatory requirements, is an uphill battle. Often, LoB leaders performing under pressure to deliver quick solutions, choose to bypass IT and hastily embrace solutions that poorly integrate with the enterprise’s existing infrastructure and overall goals and vision. This creates a fractured technology ecosystem built on poorly articulated strategies which will, at some point in time, be rendered redundant.
In such a situation, enterprises will need to either make the investment and develop their own in-house Big Data analytics practice or partner with a technology specialist and avail of analytics outsourcing. For the latter, partnering with the right cost-effective analytics agency who can also ensure data security, is the chief determiner that should be considered before making a choice. A well-considered decision in this regard will also form the key to data analytics success.
The Preference for Outsourcing Analytics
Ultimately, when it comes to making a choice between retaining an analytics unit in-house or working with an external technology partner, companies can base their decision on whether or not they can:
1. Tap into a niche talent pool
It can be a complex and often headache-inducing task to build up a reservoir of in-house tools and infrastructure for analytics. Leaders then need to scout for and hire the talent to use it. This scenario forces many decision-makers to begin considering outsourcing data analytics. A reputed and experienced analytics partner will possess niche, focused skill sets which can be leveraged quickly to meet your business goals. After carefully weighing the benefits and risks of outsourcing data analytics, it soon becomes clear that it’s the only way to catch up quickly with early adopters who had the foresight, time and resources to experiment, develop, and scale up their business intelligence analytics practice.
2. Leverage deep domain expertise
A dedicated analytics expert’s domain experience in solving analytics challenges, puts them in a much better position to tackle similar initiatives efficiently. By working in parallel with the in-house team, an outsourcing partner can ensure a particular task is completed with only a minimal number of iterations, leveraging industry best practices that are in alignment with your goals and expectations. Specialists will also be able to import learnings from other implementation initiatives to guide and train the in-house team so that they can eventually assume full command over their Big Data analytics program.
3. Assured data security
More often than not, companies are held back by their concerns around data security and regulatory compliance issues. This is particularly true in the BFSI sector. Since customers consent to sharing their data only with the enterprise, outsourcing data analytics raises serious issues around privacy and confidentiality. However, with the right data governance model in place, companies can safely give an external agency access to their repository without running the risk of corporate espionage or falling victim to cyber-attacks. They can do this by availing of on-premise solutions or even remote access, where vendors only given read-only access to sensitive data.
4. Seamlessly comply with evolving regulatory standards
It’s an often overlooked fact that external technology partners need to meet certain regulatory requirements themselves, and assume equal, if not similar levels of liability in case there is a data breach. Specialized analytics firms are usually armed with the latest toolkits and technologies which are often superior and better equipped to handle new-age threats to data security.
5. Access cutting-edge tools and technologies
Companies with limited business intelligence budgets can only invest so much into acquiring expensive licenses for proprietary analytics platforms. Without an adequate implementation roadmap in place, both software licensing costs and the enterprise application ecosystem can spiral out of control as the organization continues buying analytics tools on a need basis. However, by outsourcing their analytics practice to a technology partner, businesses can access the latest technologies without having to bear any fixed annual costs or spend on refreshing their own toolkits to stay up-to-date.
Choose Once and Choose Well
Data analytics is no longer an isolated vertical business operation and leading global brands have long stopped thinking of it that way. The impact of analytics extends across departments and is directly tied in to overarching business goals. Analytics is an umbrella term for a wide spectrum of tools, techniques, services, and core statistical expertise. While an in-house analytics operation allows business leaders to cherry-pick the people, tools and processes that will be deployed to perform analytics, it can be a costly, time-consuming and not always effective affair. An external agency, on the other hand, might be better placed to deliver customized horizontal and vertical solutions packaged in specific configurations to meet your unique business needs. By offering scalable and agile Big Data analytics models, even external analytics partners can quickly adapt to the unique needs of small, medium, or large organizations at a competitive cost and time.
Ultimately, brands need to choose a model that works best for them. Because whatever the decision, it is no longer a matter of debate that arming yourself with robust analytics is the key to succeeding in a data-driven world.