Experts have proclaimed the end of the big data bubble for a few years now. Yet more and more companies want to be analytics-driven with analytics-oriented departments to take full advantage of the exponential growth of data points.
It's reported that marketers will spend an estimated $11.5 billion in 2015 on data and related solutions alone. Marketing, advertising and sales success hinge on back-end activities like analytics, collection and governance to predict and influence outcomes that impact the bottom line.
To harness and convert data into stronger business strategies and overall profitability, approach data practices with a holistic integration of people, process and technology, following three key steps: collection, strategy and alignment.
An intelligent, processed approach to data strategy starts with collection. According to IDC, 90 percent of all current digital data is unstructured, and by 2020 data will have reached 40 Zettabytes in size. For these reasons, a foundation of sound data practices that differentiate between superfluous and smart data is necessary.
Start with a clear understanding of project goals and requirements to guide the collection process. Establishing this helps ensure data collected is “smart” or meaningful. Collection shouldn’t narrowly focus on new data. Many organizations already have a goldmine of owned data that should be tapped. To make the most of historical data, scan legacy systems, such as social pages or purchase history, map findings back to strict uniform terminology, and fill in the gaps where data is missing across the organization.
Having a process for collecting new data and examining historical data up front ensures quick and accurate collection, minimizing time spent on governance practices and carving down unnecessary data sets.
Once data is collected, work with data-marketing specialists to analyze and align functional uses and marketing’s business goals. This requires a team of analysts and strategists who have both high levels of industry and domain expertise to identify sources, manage collection and road-map operations processes.
Even when using smart devices and machines, the human touch as a decision maker is critical. Algorithms are useful but not all market reactions -- like emotional behaviors or sarcasm -- can be anticipated by technology. This where human intervention comes in.
Teams of analysts can help organizations identify, collect and integrate data from sources and channels, like web traffic, Facebook, Salesforce, etc., into a proprietary database. Once established on a datamart, it can be integrated into current campaign tools through human labor. Having this data integrated into marketing tools gives brand-side marketers the insights to improve customer experiences, measure performance of digital assets, predict customer decision stages, etc.
An adept strategy relies on performance measurement processes that feed back into the datamart. Agree on measurement elements and KPIs and incorporate into data strategy as soon as the goals are established. Measurement is a staple of any technologically driven program, so establish traceable KPIs based on the implemented data’s end goal.
Use a proprietary or third-party analytics platform to collect and funnel performance data back into the datamart, which perpetuates a cycle of collection, analysis, execution and measurement.
Marketer's alignment across relevant departments -- sales, IT, e-commerce, etc. -- who are key to implementation is invaluable. Sales and marketing tend to silo common data, like leads, performance metrics, sentiment, etc. But by establishing alignment from the start allows both departments to share valuable information, with data flowing freely between the two departments and creating a uniform customer experience.
Another example can be demonstrated with IT and marketing. Marketers spend more on technology than some IT departments now, but need alignment to ensure data is stored, platforms are integrated and in-house technical support is available. Alignment between these two departments appeases both marketer’s need for autonomy and IT’s domain over platforms, allowing for the integration of datamarts into other units’ datasets from the onset.
Data is a company asset, not just a departmental tool. When approached correctly, data helps align business goals with a competitive edge so organizations must ensure each department and internal structure clearly understands the objective. With the right people, process and technology in place, big data becomes a little less scary and can transform into tangible and actionable insights.