Due to improving AI capabilities, analytics and business intelligence is having a greater impact on business operations and strategy than ever before.
In listing its five tech predictions for 2019, CNBC Africa mentions AI going mainstream as one of them, particularly within the sphere of analytics and business intelligence.
The article states that machine learning is no longer the sole domain of Amazon, Apple, Facebook, Google and other major tech companies, but has democratized to a point where it is feasible to implement it in smaller, less resource-equipped organisations.
“Anyone with enough development knowledge can theoretically build their own mini-Watson, not just major enterprises with full data science teams at their disposal. Start-ups, entrepreneurs, small businesses and NGOs alike will be able to leverage existing tools to create their own AI-driven platforms and ecosystems” writes Lee Naik, CEO of TransUnion Africa.
Exciting stuff. However, this newfound ability also comes with the responsibility to make use of it where possible, with the risk of potentially being left behind in a rapidly evolving market should its capabilities be ignored.
Naik adds that “Organisations are going to need to take a serious look at their business intelligence capabilities going into 2019 and see whether they can keep up in a market where AI is everywhere. Finally, we will see the move from gut-feel to analytics-led organisations.
Efficiency, Convenience, Accuracy
“In an IT channel which is has perhaps been slow in its digital transformation, a channel platform brings efficiency, convenience and accuracy. We’ve earlier covered how digital platforms are strong on the vital aspects of efficiency and simplicity, but they’re well-suited to analytics usage as well.
In a May 2017 article, management consulting firm McKinsey stated that “Rapid technological advances in digitization and data and analytics have been reshaping the business landscape, supercharging performance, and enabling the emergence of new business innovations and new forms of competition.
“According to McKinsey, data and analytics are transformational, yet many companies are capturing only a fraction of their value. Data and analytics are disrupting business models and bringing performance benefits.
New Data-Driven Approaches
McKinsey again: “Disruptive data-driven models and capabilities are reshaping some industries, and could transform many more. Certain characteristics of a given market open the door to disruption by those using new data-driven approaches, including:
- inefficient matching of supply and demand
- prevalence of underutilized assets
- dependence on large amounts of demographic data when behavioural data is now available
“While it’s debatable to what extent these characteristics currently represent the IT channel, it’s clear that having a data-driven digital approach to key channel operations such as sales, credit management, holding stock and licensing can have an impact on driving increased revenue and cutting waste (and the associated costs thereof).
McKinsey also mentions that one of the key components to an effective data and analytics transformation is “Solving for the problems in the way data is generated, collected, and organized.
Many incumbents struggle to switch from legacy data systems to a more nimble and flexible architecture that can get the most out of big data and analytics. They may also need to digitize their operations more fully in order to capture more data from their customer interactions, supply chains, equipment, and internal processes.
“Of course in order for digital transformation to occur, there needs to be the requisite organizational buy-in from the top-down. Commitment to overhauling legacy systems and manual processes is a time-consuming and disruptive process, and management needs to be able to clearly visualize and believe in the long-term benefit before giving the go-ahead.
This transformation however, does seem to be picking up momentum. According to Forbes, there is an emerging trend to bring customer data into one place, commonly referred to as the customer data platform, or CDP.
The Customer Data Platform (CDP)
Gartner defines a CDP as “a marketing system that unifies a company’s customer data from marketing and other channels”, as companies “seek an effective way to efficiently collect, manage and use their first-party customer data.”
Forbes also reports that these platforms are beginning to see widespread adoption across enterprises, with some 78% of organizations either having, or developing a customer data platform.
CDPs are generally broader and aggregate their customer data from a wider set of sources than traditional CRM systems, and with so many customers oscillating between phones, tablets, game consoles, desktops and laptops, many marketers are desperate for a unified view of the customer.
Channel distribution platforms are well suited to acting as CDPs and have a natural advantage in that transactional data from all sources are aggregated within the platform, no matter the original source.
Because all channel business transactions are driven through the aggregation platform, channel businesses utilizing such platforms thus have a natural, ‘in-built’ CDP with which to work.
The transactional flow of data through the distribution platform quickly builds up a valuable source of information which marketers can then direct towards their marketing and sales initiatives, via analytics interpretation.
Whether businesses recruit and utilize the new and upcoming AI tools to assist them in this endeavour is up to them, but in either circumstance its good to know that the base data collection platform is already there and well-suited to further analytics usage.