Deriving value from data is a top priority for enterprises today, as they face difficult decisions on the road towards modernizing their business. In this ongoing series, I look at how data can serve to contextualize business decisions and achieve exponential value from investments. If you’re looking to know more about what is contextualization in IT and why you need it, read Part 1 here.
Inefficiencies in Your IT Landscape That Could Benefit from Contextualization
Enterprises are now in the midst of flux, as IT systems witness an overhaul, customer expectations evolve, and new business opportunities open up. Inevitably, there is a risk of becoming inefficient in key areas such as security, process performance, marketing, asset management, and contact centre operations. Contextual data could make a significant positive difference across all these verticals.
For example, a system that continually monitors the IT landscape for user behaviour patterns will immediately spot an anomaly. This can then be placed in the context of other parameters (historic behavior by the same user, the application in question, etc.) to merit further investigation. The chances of overlooking a breach fall dramatically — and the enterprise doesn’t have to spend time or effort on hunting down irrelevant anomalies.
Similar use cases are possible in marketing, asset management, process automation, and contact centers as well. In a nutshell, contextualization takes raw data from a variety of structured and unstructured sources and passes it through an analytics layer to generate insights. These insights can then be fed into the IT system to automate the next best move.
If you’re interested in the macro impacts of contextualized data-driven decision-making, take a look at a Harvard Business Review study that I discussed in Part 1.
Contextualization Trends and Statistics to Remember
Companies across the world are investing heavily in contextualization capabilities, either to support internal enterprise operations or to augment their product offerings. In 2018, Microsoft went public with its IoT-based security solution that would grant users access based on real-time contextual data. In 2017, Cisco acquired MindMeld with the explicit purpose of gaining contextual knowledge on customers via AI techniques.
And Google made headlines with its acquisition of Looker, for over $2.5 billion to obtain contextual analytics capabilities — given the company’s focus on data and data-driven products, this makes perfect sense.
Apart from this, the context-aware computing market is showing a CAGR of 30%, which is markedly higher than most other technology segments. And as early as 2014, 32% of CIOs in retail were planning to invest in contextualization over the next 18 months.
The writing is on the wall: contextualization is poised to be the foundational framework for business decisions over the mid and long-term. This will be enabled by four leading companies:
Shining the Spotlight on Amazon, IBM, Oracle, and Google
Amazon’s area of expertise lies squarely in IoT. AWS IoT Analytics has powerful contextual capabilities and can be plugged into data sources like temperature, motion, or sound. Interestingly, this can be integrated with the Salesforce platform to bring IoT-led contextualization into the CRM space. Interactions between customers and AWS services are fed into Salesforce to generate contextual insights.
IBM also has an advanced analytics offering with contextualization capabilities. It partnered with Volkswagen to create an on-demand workplace, informed by contextualized event information. This (along with other solution features) boosted Volkswagen’s procurement staff productivity by 20%.
Oracle has turned its eye on customer insights and market intelligence, aided by contextual data. It worked with a sports entertainment company, World Wrestling Entertainment (WWE) to curate content — based on which keywords and stories were trending with the targeted fanbase at the moment. This resulted in a 351% higher conversion, owing to the underlying Grapeshot technology — Grapeshot can also bring contextualization to brand safety and GDPR.
Finally, Google has several contextualization capabilities inside and outside of marketing. It helped telematics company Geotab, convert raw data from fleets into valuable insights for customers, complete with context-specific benchmarks. Google’s TensorFlow offering was instrumental in achieving this.
Examples like these are paving the way for the widespread implementation of contextualized processes. In the next installment, I explore how enterprise leaders can introduce contextual capabilities on their IT systems in a way that translates into genuine business value.