Big data is passe! Move on to intelligent data

Sesha Rao

Enterprise Applications have evolved from traditional workflow automation to productivity enhancing intelligent applications. The Leaders of Enterprise Computing agree that the future is centered on intelligent applications, data science, and analytics.

Satya Nadella, CEO of Microsoft, in his strategy document said, ''We will shift the meaning of productivity beyond solely producing something to include empowering people with new insights. We will build tools to be more predictive, personal and helpful. We will enable organizations to move from automated business processes to intelligent business processes…” 

Marc Benioff, CEO and Founder of Salesforce.com in an interview said, ''When I look at the next set of technologies that we have to build in Salesforce, it’s all data-science-based technology. We don’t need more cloud. We don’t need more mobile. We don’t need more social. We need more data science... ''

It is obvious that the data quality and hygiene is paramount since the outcome produced by algorithms is directly proportional. The challenge now comes not in the technology, but the data itself.

The Importance of Clean and Complete Information

Intelligent enterprise applications face these issues in retail and B2C marketing. We are all familiar with targeted advertisements from online marketplaces. They send us recommendations for similar products that we had bought previously. We often wonder ‘how do they know so much about me’. They know so much about us based on the information ‘we provide’ them. Hence those systems are very dependent on the quality of the underlying information.

Many of us had this experience – recently I rented a Jaguar from a local retailer for a day, as a birthday surprise for my son. After a while I started receiving targeted advertisements on super luxury high-end cars, which I would never buy due to my lifestyle. There is nothing wrong with those data science algorithms, they are just working on non-relevant information. I am not a good target.

The world of intelligence extraction from abundant information in the B2B enterprise is much more challenging than it is for B2C companies. Those services rely mostly on the first-party data that we agreed to share. B2B Enterprises don’t have complete and continuous engagement with potential customers that B2C Enterprises have with their retail customers. Hence they need to depend on two primary sources - first-party data (collected by the enterprise) and third-party data (sourced by external providers).

The problem is that B2B first-party data decays quickly. Historical metrics suggest that data decay is close to 25pc every year. This is primarily because employees change jobs, change roles or titles. firmographic data of companies frequently change too -- their sales figures go up or down, their employee count up or down, they change their names, or merge with another company, they go public or private or go bankrupt. At this rate at 25pc change each year suggests that within four years 100pc of the data will be wrong. This is where the importance of quality really comes in.

Third-party data is used by enterprises to augment first-party data. Traditionally companies looked for quantity (e.g. give me more names, more leads, more companies); but today the focus has shifted away from quantity to quality (e.g. I need accurate data, and as much insight on this person/company as I can get). The struggle to obtain and maintain information quality and accuracy is universal.

The Informed Enterprise

Data science technologies that are fully supported by Predictive and Prescriptive engines can make recommendations. They help us see where to look for potential opportunities – how to unearth the needle in the haystack. This evolution will result in guiding the CRMs from the past into the future; from a workflow and automation focus to intelligence and prescription focus.

We see customers apply data science to improve their lead scoring, customer engagement both in frequency and quality, and their product reach by better messaging. Currently most of these efforts are focused on silos of data (inbound lead flow, customer interactions, targeting databases), but they need to evolve to integrate much more near-real time information available about prospects, customers, and their interests.

Right Insights are the Key

How do we prepare ourselves for an informed world, so that we are a fully informed enterprise and make use of significant productivity enhancements? We need to create a reservoir of knowledge that powers data science and predictive algorithms:

1. Does our CRM have clean, normalized data? If we have the same company or person represented in multiple and disconnected records, no algorithm in the world will be able to make sense of that information.
2. Does our marketing database have the up-to-date and normalized information that we need to segment, score, and route leads?
3. Do we have a way to maintain the marketing and CRM information up-to-date (near-real-time)?
4. Do we have the additional data that represent buying signals, mapped to the right companies and people? Signals include both behavioral information (is this company hitting our website) and demographic/firmographic information (does this company fit our qualifying criteria? Is this the right decision maker, and if not who is?)

If we start with a clean dataset, rich with the right signals, any of the great predictive and analytic technologies available in the market will do a great job for our businesses. Otherwise, trash in will inevitably result in trash out – and it will continue to be a noisy world.

The author is MD, India Operations, InsideView