“IT-driven business transformation is always bound to fail” – Amber Storey, Sr Manager, Ernst & Young
The value of Big Data-driven Analytics is no longer in question both from a customer as well as an enterprise standpoint. Lack of investment in an analytic strategy has the potential to impact shareholder value negatively. Business Boards and CXOs are now concerned about their overall levels and maturity of investments in terms of business value – i.e increasing sales, driving down business & IT costs & helping create new business models. It is thus an increasingly accurate argument that smart applications and ecosystems built around them will increasingly dictate enterprise success.
Such examples among forward-looking organizations abound across industries. These range from real-time analytics in manufacturing using IoT data streams across the supply chain, the use of natural language processing to drive patient care decisions in healthcare, more accurate insurance fraud detection & driving Digital interactions in Retail Banking etc to quote a few.
However , most global organizations currently adopt a fairly tactical approach to ensuring the delivery of traditional business intelligence (BI) and predictive analytics to their application platforms. This departmental is quite suboptimal in ways as scaleable, data-driven decisions and culture not only empower decision-makers with up to date and real-time information but also help them develop long-term insights into how globally diversified business operations are performing. Scale is the key word here due to rapidly changing customer trends, partner, supply chain realities and regulatory mandates.
Scale implies speed of learning, business agility across the organization in terms of having globally diversified operations turn on a dime thus ensuring that the business feels empowered.
A Quick Introduction to Business (Descriptive & Predictive) Analytics
Business intelligence (BI) is a traditional and well-established analytical domain that essentially takes a retrospective look at business data in systems of record. The goal for BI is to primarily look for macro or aggregate business trends across different aspects or dimensions such as time, product lines, business units and operating geographies.
BI is primarily concerned with “What happened and what trends exist in the business based on historical data?“. The typical use cases for BI include budgeting, business forecasts, reporting and key performance indicators (KPI).
On the other hand, Predictive Analytics (a subset of Data Science) augments & builds on the BI paradigm by adding a “What could happen” dimension to the data in terms of –
- being able to probabilistically predict different business scenarios across thousands of variables
- suggesting specific business actions based on the above outcomes
Predictive Analytics does not intend to nor will it replace the BI domain but only adds significant business capabilities that lead to overall business success. It is not uncommon to find real world business projects leveraging both these analytical approaches.
Creating an Industrial Approach to Analytics
Strategic business projects typically begin imbibing a BI/Predictive Analytics based approach as an afterthought to the other aspects of system architecture and buildout. This dated approach then ensures that analytics becomes external to and eventually operating in a reactive mode in the operation of business system.
Having said that, one does need to recognize that an industrial approach to analytics is a complex endeavor that depends on how an organization tackles the convergence of the below approaches:
- Organizational Structure
- New Age Technology
- A Platforms Mindset
Illustration – Embedding A Culture of Business Analytics into the Enterprise DNA..
Let's discuss them briefly:
The historical approach has been to primarily staff analytics teams as a standalone division often reporting to a CIO. This team has responsibility for both the business intelligence as well as some silo of a data strategy. Such a piecemeal approach to predictive analytics ensures that business and application teams adopt a “throw it over the wall” mentality over time.
So what needs to be done?
In the Digital Age, enterprises should look to centralize both data management as well as the governance of analytics as core business capabilities. I suggest a hybrid organizational structure where a Center of Excellence (COE) is created which reports to the office of the Chief Data Officer (CDO) as well as individual business analytic leaders within the lines of business themselves.
This should be done to ensure that three specific areas are adequately tackled using a centralized approach-
- Investing in creating a data and analytics roadmap by creating a center of excellence (COE)
- Setting appropriate business milestones with “lines of business” value drivers built into a robust ROI model
- Managing Risk across the enterprise with detailed scenario planning
New Age Technology
The onset of Digital Architectures in enterprise businesses implies the ability to drive continuous online interactions with global consumers/customers/clients or patients. The goal is not just to provide engaging visualization but also to personalize services clients care about across multiple modes of interaction. Mobile applications first began forcing the need for enterprise to begin supporting multiple channels of interaction with their consumers. We have seen how exploding data generation across the global economy has become a clear and present business and IT phenomenon. Data volumes are rapidly expanding across industries. However, while the production of data itself that has increased but it is also driving the need for organizations to derive business value from it. This calls for the collection & curation of data from dynamic, and highly distributed sources such as consumer transactions, B2B interactions, machines such as ATM’s & geo-location devices, click streams, social media feeds, server and application log files and multimedia content such as videos etc – using Big Data.
Cloud Computing is the ideal platform to provide the business with self-service as well as rapid provisioning of business analytics. Every new application designed needs to be cloud-native from the get go.
The onset of Digital Architectures in enterprise businesses implies the ability to drive continuous online interactions with global consumers/customers/clients or patients. The goal is not just to provide engaging Visualization, but also to personalize services clients care about across multiple modes of interaction. Mobile applications first began forcing the need for enterprise to begin supporting multiple channels of interaction with their consumers. For example, banking now requires an ability to engage consumers in a seamless experience across an average of four to five channels – Mobile, eBanking, Call Center, Kiosk, etc.
A Platforms Mindset
As opposed to building standalone or one-off business applications, a Platform Mindset is a more holistic approach capable of producing higher revenues. Platforms abound in the webscale world at shops like Apple, Facebook, and Google, etc. Applications are constructed like lego blocks and they reuse customer and interaction data to drive cross sell and up sell among different product lines. The key components here are to ensure that one starts off with products with high customer attachment & retention. While increasing brand value, it is key to ensure that customers and partners can also collaborate in the improvements in the various applications hosted on top of the platform.
Business value fueled by analytics is only possible if the entire organization operates on an agile basis in order to collaborate across the value chain. Cross-functional teams across new product development, customer acquisition & retention, IT Ops, legal & compliance must collaborate in short work cycles to close the traditional business and IT innovation gap. Methodologies like DevOps who’s chief goal is to close the long-standing gap between the engineers who develop and test IT capability and the organizations that are responsible for deploying and maintaining IT operations – must be adopted. Using traditional app dev methodologies, it can take months to design, test and deploy software. No business today has that much time—especially in the age of IT consumerization and end users accustomed to smartphone apps that are updated daily. The focus now is on rapidly developing business applications to stay ahead of competitors that can better harness Big Data’s amazing business capabilities.
Enterprise-wide business analytic approaches designed around the four key prongs (Structure, Culture, Technology, and Platforms) will create immense operational efficiency, better business models, increased relevance and ultimately drive revenues. These will separate the visionaries, leaders from the laggards in the years to come.