Organizations are challenged with continuing to develop, deploy and leverage data strategies while keeping pace with the ever-changing nature of analytics, technology, and an unstable global business environment. Tracking the trends has never been more important.
To that end, Aria gathered a panel of analytics experts from the worlds of business and academia for an exclusive roundtable discussion, designed to provide our customers with insights into the state of analytics today and trends critically relevant for any data-focused organization. Our experts from Aria Systems, Berkeley Fisher Center for Analytics, Intel, and Kennesaw State University shared the following thoughts:
Data Democratization Accelerates
In the early days of business intelligence, analytics tools were far from user-friendly or understandable by non-technical people. Data scientists were needed to capture data and produce and deploy reports across the organization. With opportunities to leverage AI, data and machine learning growing exponentially, organizations began to realize that their ability to harness analytics to produce real outcomes was going to be hampered unless more employees had access to and a degree of familiarity with the tools used by the experts, ushering in an era of data democratization. As analytics programs evolve, success in a digital environment will continue to be even more reliant on getting data into the hands of people who are making decisions, interacting with customers, and directly involved in manufacturing or product development processes without the constant intervention of an expert.
Data Literacy is a Shared Responsibility
The increased use of analytics and reliance on data is a good thing. Companies are leveraging the power of data to anticipate, predict and deliver customized and personalized experiences to customers. That said, as analytics has become more pervasive across organizations, the gap in data literacy has become more pronounced. Without basic foundational knowledge, employees can become frustrated and revert to their old processes, and the model will break down. Data experts often believe it’s the sole responsibility of non-technical employees to familiarize and educate themselves in the language of data. To an extent this is true, and companies need to elevate the level of digital literacy across the general employee population. For analytics programs to thrive, however, the job falls equally on experts to ensure that reports are accessible to and digestible by their audience and can therefore be put into action.
Embedded Analytics for the Non-Techies
Analytics has been behind enterprise applications for a long time. For example, typical CRM packages are going to have response modeling and complex segmentation built into the solutions. Today, the embedding of analytics models, AI and machine learning is expanding to the point where users are not even aware of their existence. A non-technical creative person using Adobe’s cloud suite probably has no interest in knowing how the analytics engine is working in the background, generating suggestions, or helping them make better decisions based on behaviors and predictive data. And that’s just fine.
Analytics Agility Required
When business dynamics change, even the most successful analytics models will be impacted. This has been particularly evident during the pandemic. Multiple industries, particularly retail and food service, saw their business models upended by Covid. Retail stores became fulfillment centers, while formal, sit-down restaurants turned to delivery and take-out services for survival. These changes necessitated a refinement and retuning of analytics approaches and the development of new data streams that can be shared with and extended to different parties and partner organizations. Among other things, the pandemic demonstrated that analytics models working today will not necessarily work forever. Agility and pliability are needed to respond quickly to changing tides.
Effective Models Start with Questions
The creation of successful analytics programs begins with asking the right questions. Companies and research leaders must understand from the outset what you are looking for and trying to achieve. The answers to these questions will allow data analytics strategists to understand what data need to be captured and processed, how it needs to be visualized and applied across the organization. Determining the right questions starts with an honest assessment of the business challenges that need solving and investigating the areas where problems may exist. It also means overcoming inherent biases by involving a diverse group of people in the process. In the end, asking the right questions leads to the right insights and, ultimately, innovation.
Leveraging No/Low-Code Opportunities
The emergence and availability of no-code and low-code tools have contributed to the democratization of data, as previously mentioned. Open-source communities have developed robust collections of models and tools that can be used by individuals of varying levels of data proficiency. These tools save time and money, and enhance efficiency, precluding the need for companies to develop their own code. No-code models have potential and will work well when the right data is added, but they can backfire without the implementation of strict guardrails. Before bringing these tools into their data flows, companies should conduct a thorough examination to ensure they can and will be used in ethical and legal ways and establish strict governance to avoid the potential for reputational and financial damage.
The Power of Anonymized Data
Companies crave access to individual data for marketing to potential customers on a highly personalized level. But the power of anonymized data should not be underestimated. Analytics programs are often more about finding patterns, and this can be accomplished with great effectiveness using anonymized data. Furthermore, with the continued emergence of innovative solutions designed to protect privacy, combined with stringent compliance restrictions, deploying anonymization techniques and strategies can allow companies to capture information, categorize data, segment customer groups, identify habits and inform strategies with less risk.
