Enterprises understand the importance of having an analytics tool that gives them a complete understanding of their customers, and the ability to communicate those insights and offer specific actions and next steps at scale empower the organization as a whole. The challenge for many brands is that the ones who touch the data, data scientists, are often siloed within a company.
Those who might need be able to usethe data in meaningful ways, such as IT employees, might not have access. Businesses grapple with scarce resources against the need to analyze the incredible amount of data that is collected--never mind the need for business insights to drive efficiencies and growth.
It’s clear that the data scientist role has become increasingly important for any brand trying to stay relevant. But as more data becomes readily available and brands are able to understand that full customer journey, the focus will shift from data scientist to IT. The data science skillset is no longer important for only the employees who work within data, but also for anyone in the IT world. This is an opportunity for the IT organization to shine.
In this eWEEK Data Points article, Ben Gaines, who has a track record with Adobe Analytics of providing brands with enterprise-grade analytics, offers readers his seven best practices for reshaping the world of data and IT.
Data Point No. 1: IT's data estate will continue to grow.
As a stakeholder for all other teams inside an enterprise, IT is the most logical group to have responsibility for breaking down data silos and bringing everything together in one place for analysis and action. As IT organizations look outward, they will find that disparate teams have a lot more customer, operations and transactional data than they imagined. IT will need strong data strategy and governance to bring all of these points together.
Data Point No. 2: IT is best positioned to take the lead on issues of consumer privacy and data security.
As a traditionally governance-oriented organization, IT can bring much to the table to ensure that all other teams in an organization are following both best practices and legal requirements around the collection, storage and use of customer data.
Data Point No. 3: CIOs should take ownership of bringing together customer data.
HBR recently reported that 72% of C-level executives polled said that they have yet to forge a data culture, and 69% have not yet created a data-driven organization. In many companies, there is a vacuum of centralized data leadership, often despite the efforts of a chief data officer. IT, with its technical aptitude and focus on process, is best equipped to step into this leadership role.
Data Point No. 4: IT should become familiar with stakeholders' data needs and use cases.
Your data strategy will not succeed if stakeholders do not believe that their needs will be met by an IT effort to enable data-driven decision making. If marketing's priority is cross-channel personalization using machine learning models to determine next best offer, then IT must seek to understand and prioritize that use case against other company priorities. Simply tossing a bunch of data into a lake isn't enough. IT's approach should be based in a multi-year view of what other teams need to be able to do with data and the ROI that teams expect to achieve from these activities; investments should align with that view such that IT is headed in the same direction as its stakeholders.
Data Point No. 5: The customer intelligence revolution positions the IT organization to increase in value.
The customer intelligence revolution may sound like a marketing- or product-led trend, but it may be the IT organization that has the most to gain. Bring all of the company's data together in one place and facilitating reporting, analysis, and insight puts the CIO squarely at the front of establishing a data-driven culture and improving the customer experience. Investing in breaking down data silos and taking the lead on bringing peers to the table to design/implement a strong corporate data strategy is fully in the CIO's grasp, and it will only raise the importance and value of the IT organization.
Data Point No. 6: IT needs to ramp up its data science chops for data governance as well as obtaining value from combined data sets.
While many IT organizations possess deep technical knowledge, the challenge of bringing together massive and complex data sets from a wide variety of peer teams may require investment in new skill sets. Data science is a lot more than machine learning, although stakeholders will require ML/AI help as well—it includes data wrangling, data munging, and data visualization skill sets which may be shorter supply in some IT organizations.
Data Point No. 7: Brands will move customer data to the cloud to take advantage of scale, services.
Five years ago, many brands swore that they would never move customer or operations data to the cloud. This reticence was grounded in security concerns and the inability of cloud vendors to solve certain key use cases, leading brands to wonder why they would bother to move all of that data elsewhere. More recently, cloud vendors have made huge leaps forward in addressing both of these areas, adding numerous services both to connect data to decision-making and real-time actioning. This is both for internal as well as customer-facing use cases as well as enhancing the security of customer and key business data in the cloud through enhancements such as shielded VMs and credential management tools. While there may still be some advantages to an on-premise data store, these are dwindling as cloud vendors see the value of securing and scaling data for brands.
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