If organizations drown in data, it is worth setting a positive alarm. And there is a valid reason. Big data, machine learning, advanced analytics and Artificial Intelligence promise translation of data into insights, into business results and value. And when there is data explosion, compounded by ordeals, challenges and hurdles in translating data into business results and value, how would companies anticipate and overcome challenges in focusing on data that matters, using the right data to address specific business problems and convert data into business results.
What challenges do organizations face to convert data into business results and value?
‘The Right Data’ pandemonium
This is how the data imperative dictates – Bring the ‘Right Data’ to support specific business objectives. Before jumping the data gun, what becomes the focal point is the business problem that an organization is trying to solve, and the desired results the organization aims to achieve. In establishing this prerequisite, organizations get the head start to lay the course required to acquire the right data. All other data parameters – nailing down data sources, acquiring and storing data, assuring data quality and rolling out the analytics initiative – will fall in place once this prerequisite is established.
The Right data, and big data, then becomes the feed for the analytics engine to drive desired business results.
Say, we are trying to address the business problem of customer complaints. If an organization identifies data on social platforms as the right data to address this problem, using the right data depends on how this business problem is connected to technical components in terms of sourcing and analysing the data and how the data pipeline and roadmap are laid down.
Business comes first, then comes the data and analytics.
Business First Analytics Next
Good turnarounds in Data and Analytics initiatives are established by keeping the business first. It is critical to know what business problems need to be solved, what opportunities need to be analyzed, and what KPIs matter most to achieve desired business results. Data, big data and analytics comes next to support this cause.
An apparel retailer launched into a conversion drive but achieved marginal improvements in customer conversions. Having implemented footfall analytics to act on insights around shopper to customer conversion, it fell short on the ‘fitting-room’ front. Here was an opportunity to analyse conversions and strengthen the cause of increasing customer conversions. The shopper traffic into the store would have been better augmented by reading the traffic into fitting rooms, strengthening insights around shopper switch to customers from the fitting room.
The retailer had not articulated this problem proficiently – the problem of why more number of shoppers are not getting converted into customers. In other sense, it was a missed opportunity – Taking a closer look at the traffic pouring into the fitting room and the conversion from thereon would have better served the purpose of increasing customer conversion rates.
Mountains of data, molehills of insight
There is a business problem to be solved. Yet data used for addressing the problem eludes enterprises, as in the case of answers to some searching queries like where to source required data, and how to access data. This puts the emphasis on Data democratization, which helps organizations conquer the ordeal of generating insights from huge data volumes. Data identified for a specific business problem might be owned by a different department, for instance. Siloed data ownership can lay the hurdle to extract insights from data.
Data in silos, departments in silos and organizationally sioled data lay roadblocks in converting data to business insights and results. This could arise from the failure to tap into data across departments for building critical metrics, high costs involved in acquiring datasets from other departments and ordeals in managing data coming in different formats from different sources.
When systems like shared data acquisition framework, standard data governance and data catalogs are established, it becomes effective for cross-functional teams to use relevant and the right data and analytics to support a business objective and promote desired outcomes.
Legacy barrier to Modern data architecture
Take stock of the data situation, and are there legacy barriers blocking the road to insights?
In this scenario, backtracking the data need leads to the most critical business problems that need to be solved during the subsequent business cycles. For instance, an organization could plan for a successful product launch, facilitated by the diligent use of data.
With a renewed data strategy, organizations brainstorm technologies driving the data strategy, determine the need for new technologies and identify overhauling opportunities in terms of data management, information, and analytics systems.
Say, when there is a need for additions such as Data lake, real-time offers, and customer personalization, the shift to modern data architecture is rolled out. For instance, Amazon S3 fits in well to facilitate infinite scaling and speedy deployment. Organizations are also hit hard by the overbearing costs to support maintenance of data architecture with low returns, and the lack of robust technology infrastructure for retrieving, storing and analyzing data from different sources at scale.
From on-premise to cloud transition, real-time data processing, extensible data schemas, and modular data architecture, organizations can embrace the most fitting option, as their need dictates, to make the transition to modern data architecture and convert data to desired business outcomes.
Saksoft’ s ‘Discovery’ to Enhanced Data Yield Initiative
A constantly changing data environment at a leading Telecom company challenged the determined big data experts and data architects at Saksoft. The team worked across 50+ departments, gauged challenges facing the Telecom company in using the existing data architecture to manage data growth, turn data into better business results.
Saksoft’ s data probing led to valuable Discovery to Enhanced Data Yield results. The team guided the Telecom major to make better use of unused data to build granular KPIs and achieve desired business results, such as the ones created around customer and the activation process. Data was turned into a valuable asset when the team accelerated O2A process (order to activation) by unearthing resource hurdles, and using data to turn proactive in optimizing resource optimization. The business result was that O2A Cycle time improved from 67+ business days to 46 business days. And when customer service requests were not fulfilled within four days, the team found that customers weren’t using the services.