Artificial Intelligence (AI) is the buzzword nowadays. Organizations across the globe are pouring investments worth billions of dollars into data and analytics. And yet, a lot of CXOs find themselves grappling with the most basic, yet critical questions, like 'How to start?', 'Where should the investments go?', and 'How long before they start seeing returns on their investments?
With the increasing pace of digitization, everchanging demands of customers, and the rapid pace of market disruptions and opportunities, most organizations are becoming aware of the role of data and analytics, now more than ever. However, there is still a significant gap between mature analytics practitioners and late entrants, who are trying to catch up.
As it is with most things in this world, starting off is the most tedious task (Organizational inertia!). Among various other challenges, organizations need to 'select critical business problems to target, analytics feasibility, and potential impact. Further complicating this journey is a plethora of nascent (yet alluring!) AI solutions out there, and success might always not be imminent. Here, we discuss some of the potential roadblocks and possible ways to sidestep them.
Sizing the Pie
It's all 'covfefe
Before trying to 'seize' the pie, it is important to have an adequate idea about the 'size' of the pie. Yes, we come across a lot of potential use-cases and disruptions to be unleashed by 'Data' in every industry (Brace for Impact!). Should we be worried? Is now the right time to act? Research indicates so.
Analytics does have a wide appeal in multiple functional areas ranging from customer-facing roles (sales, marketing, customer support) to operations (finance, manufacturing, supply chain). Still, it is crucial to align and prioritize efforts in the direction of business goals because each business unit within an organization requires a different approach and analytics maturity. Want to automate back-office tasks? Increase marketing RoI? Optimize inventory levels? An assessment of the organization's capabilities and constraints helps shorten that wishlist and identify where the initial focus should be.
The storming of the Bastille!
Yes, this is where we get to complain about corporate culture. Gut-based decision-making, multitudes of excel reports, never-ending budget forecasts, and out-ofthe-world sales targets! But things could be better if all decisions were backed by data and facts so that everyone could see the underlying rationale, contribute to, and support the decision-making process
While commitment from the top leadership is vital, a top-down mandate alone can't ensure that analytics is widely used for decision-making throughout the business. The top-down approach must be aligned with a bottom-up adoption to embed analytics into the way the organization thinks, decides, and acts.
Know Thy Hooman
Terminator Genisys: Fate of Humans
Even if the data and analytics roadmap is in place, a skilled workforce (yes, that too!) is needed to make sense out of data. With an ever-changing landscape and fasterthan-ever evolution in the field of AI and data tools and infrastructure, hiring skilled analytics resources is already a challenge. Moreover, retention and up-skilling the resources can be very demanding (in terms of both – time and finances) for an organization.
It is important to have internal leaders (e.g., a Chief Analytics Officer), or external partners who can drive the analytics journey for your organization. Some of the popular approaches out there include centralized units (CoEs for analytics), localized analytics units for business functions, and external analytics partners. While inhouse units may provide more control over operations and data, setting them up is a very time and resourceintensive process. External partners can mitigate talent and personnel risks by delivering access to leading analytics practices with reduced costs of ownership.
Error 404: Data Not Found!
Life is messy, but your data doesn't need to be.
Data (or lack thereof) can be the biggest and most overlooked challenge when it comes to adoption of analytics. A lot of organizations don't have (or don't know that they don't have) the necessary data to perform analytics. This can be due to legacy practices, common examples of which include – data captured through physical forms, unstructured data, no scalable IT infrastructure in place to process data, and data stored in remote silos.
Data might be overwhelming at first because of associated risks, including security, compliance, and financial (IT cost-related) risks. Most of these risks can be mitigated using proper data management, data governance, and cybersecurity guidelines. Apart from prioritizing data collection and digitization from existing sources, new data sources should be explored, while also enhancing data accessibility for all key stakeholders.
Seeing the light of day
Well, analytics solutions need analytics users as well (chicken-egg problem!). While innovation and pilot testing are what make an organization's analytics infrastructure competent, company-wide adoption is the only way to derive value out of the investment. It might require significant effort towards change management, including training on 'Why analytics?' and 'How to incorporate analytics into regular processes, governance, and strategy'.
Lastly, it is important to stay relevant and lean (you'd have to excuse this blog). There is no one-size-fits-all strategy (not even blockchain!). One needs to figure out what works and what doesn't, and this should be a sustainable and self-improving process.
The content of this article is intended to provide a general guide to the subject matter. Specialist advice should be sought about your specific circumstances.