As data growth compounds, organizations face the challenge of deciding how to invest in their future data strategy. While it may be tempting to jump to developing a transformation strategy, the foundational steps of automation and investment in new data can often get lost in the glamor of “transformation.” Devo’s latest white paper, Operational Data: A Roadmap to Value Creation, reviews how organizations can evaluate their data maturity and how they should invest in their data strategy. Between infrastructure and data management costs, talent acquisition in data analytics and engineering, and the necessary strategic commitment, the decision is one that will shape the overall direction of an organization.
So, where is your organization most well-suited to take advantage of its data: through automation (improving operational efficiency and productivity), the development of new information (investing in new data to better inform decisions with business telemetry), or business transformation (the development of new products and services to drive innovation and new revenue streams)?
Automation
Automation generates ROI by streamlining business processes and improving operational efficiency, thus reducing costs. Amazon dominates markets where it can use its automation muscle to create efficiency at scale. One example is its use of Kiva robots to automate warehouse picking operations – a use case so compelling, Amazon acquired Kiva. The robots move shelves to and from picking stations so workers don’t have to walk up and down aisles in search of products. Kiva robots use data, with ML, to decide how best to optimize shelf contents and positioning within the fulfillment center. Data generated by these robots is sufficiently strategic to Amazon to justify Kiva’s $775 million acquisition price.
However, the practice of automation goes beyond reducing manual labor and rote task work, and can be leveraged to further enable analytics and operations teams. The Devo Data Operations Platform leverages built-in anomaly detection to automatically detect and alert teams when anomalous behavior is seen within their environments. Using machine learning, this detection process can be automated and made more accurate over time, freeing up resources to perform more strategic analysis.
Information
If you’re after more than automation – and businesses should strive for this – generating new information to improve business operations should be your goal. Using data to create new value builds on automation’s virtues and puts an organization on the path to transformation. Telefonica used analytics data from its IPTV service to improve the customer experience for viewers, using Devo for continuous monitoring of service delivery. The telco has used analytic results to reduce the average time of customer help desk calls while also reducing the number of calls per day coming into the help desk. Not only have support costs been reduced, but home visits and customer churn have dropped, operational expenses have been reduced, and customer satisfaction has increased.
The commitment to achieving more information value from data requires that more data be collected, stored, and analyzed. Organizations may only store 20% of their data due to increasing storage and processing costs, and as a result are dropping 80% of their data – potentially missing out on insights that could inform decisions.
Transformation
The real benefits of an ROI strategy come in the third step: transformation of the business to drive innovation and product development. Organizations striving for transformative use of data are inherently data-driven: they ingest more and different types of data than counterparts, and develop a culture rooted in agility. Disney, for example, has transformed its amusement parks with data. Not only does the company monitor the flow of people minute to minute through turnstiles on its properties, it uses real-time data to track how many people are in line waiting for particular rides. Disney invested $1 billion in its Magic Band technology, so the company clearly feels the insights it gets from its second-to-second data is worth every cent, while park visitors also value the technology.
In any journey to transformation, ensuring your data operations infrastructure has the speed, scale and capacity to harness operational insights – while adapting to the complexities of your evolving business – is critical. Key to ensuring you can meet tomorrow’s challenges is the ability to scale data operations to extract insight – and value – from both real-time streaming events and historical analytics. And most critically, a successful journey requires understanding where you are today, and where you are best suited to go tomorrow.
