Three Big Data Requisites for Making Data-Driven Decisions
In today’s economy, gaining an information edge isn’t just the path to growth; it’s the key to survival.
For many companies, obtaining that edge means going up against Big Data, which often involves introducing new skill sets, creating a need for analytics, and establishing enterprise-wide strategies for leveraging and acting on all the data flowing into an enterprise.
Over the years, most brands have established disjointed methods for collecting the Big Data that continues to pour into their companies from a growing number of touchpoints. Because they lack the solid infrastructure necessary to integrate and organize this consumer information, most customer experience improvements reside upon an unsteady foundation. The inability to blend customer information into one cohesive model can lead companies to grapple with siloed data and the inability to take action.
The scope for joining the dots, gaining knowledge that supports targeted business activities, and presenting action plans that make Big Data a key opportunity to enhance the customer experience can be challenging to say the least. By creating a cohesive atmosphere that successfully aggregates the right data sources, applies the effective technologies, and shares the results across departments to act with agility, companies will gain the most accurate knowledge to make targeted decisions that are in the best interest of their customers.
To successfully make real-time data-driven decisions, brands must consider three steps:
1. Collect the right data with the most value,
2. Establish a standardized data structure, and
3. Deploy adaptive technology.
1. Extract Big Knowledge from Big Data
Many of us are already familiar with Gartner’s 3Vs of Big Data—volume, velocity, and variety of data. But, the key question is, “How do we know what information we need to extract from Big Data?”
Before canvassing the vast oceans of data from text messages, social channels, the website, contact center, and surveys, companies must first decide which big knowledge will bring the biggest value. This may require companies to first determine the problem that needs immediate resolution; like integrating customer data across channels and then focusing on the data that pertains to such an initiative like transactional and demographic data, permissions, and channel choices. Some experts refer to these smaller projects within their Big Data efforts as mining the ‘little data.’
Determining the key data needs also requires companies to create a cross-organizational team that can meet with the key stakeholders across the enterprise. This team should determine the immediate needs as they relate to the customer strategy and then identify the key channels and pieces of data to help facilitate those requirements. Not all companies need to hire a team of data scientists for their Big Data projects; many simply need to start small, dedicate a cross-organizational team, and identify the low-hanging fruit along their Big Data journeys.
2. Create a Standardized Data Structure and Action Plan
After a company has identified the data that matters most, it should establish governance around its data efforts. When it comes to enabling real-time data-driven decisioning, companies need to adopt a more strategic approach as to how they’ll use the data, how they’ll define it, and how they’ll manage and monitor it. Such a plan should establish specific data governance procedures and award data stewardship to key stakeholders.
Without data governance and stewardship, a company’s Big Data efforts can become bottlenecked and investments are wasted.
To succeed, companies must create a data hierarchy and organizational structure—one that not only takes into account the objectives of the overall organization and how it delivers the customer experience, but also the objectives of senior management. Additionally, data governance plans need data stewards, or empowered stakeholders who can make decisions, assign responsibilities across the enterprise, and identify highly targeted projects.
3. Deploy Adaptive Technology
No Big Data strategy is possible unless companies have the data assimilation tools and business intelligence technologies necessary to access, aggregate, and analyze their enterprise data projects.
By leveraging an analytic multichannel platform that can bring the necessary data into one place, make it easily sharable across the enterprise, and offer next-best actions to take on behalf of customers, companies will be well on their way to establishing a cross-channel Big Data strategy that enables real-time decisioning.
Revana’s adaptive Engagement EngineTM, for example, can help put companies on the path to making sense of their Big Data and becoming customer centric. The adaptive Engagement Engine can bring clarity to their organizations, ultimately enhancing the personalized customer experience necessary to drive desired results.
In doing so, companies not only cultivate a cohesive working environment, but they also open themselves up to relationship-building opportunities, for they can begin to generate a 360-degree view of the customer—a capability sure to boost satisfaction and loyalty.
With most data governance models, there are quick wins to be had, and long-term benefits. If your organization hasn’t devised a Big Data strategy, now may be a good time to get started. With these three steps, businesses can right their path toward understanding customer needs and wants in order to develop and implement targeted and effective customer strategies and transform their businesses as a result.
Engagement Engine is a registered trademark of Revana in the U.S. and other countries.