Data Futurology: Opsword Recap
Last week, we had the opportunity to attend Data Futurology’s Opsworld, an event which brought together data experts, industry leaders, and business executives to discuss the latest trends and strategies for data-driven operations. The focus was on removing the hype and instead focusing on the operational principles and approaches that generate business value. Over the two-day event, we participated in thought-provoking conversations covering a range of topics, from increasing the productivity of data-led enterprises to successfully integrating AI into organisations and leveraging data for social impact. Despite the diverse industries represented, several key themes emerged consistently across various speakers, and valuable insights which I share below for those unable to attend.
Clear Understanding of the Value Proposition of Data & Analytics
One common theme centred around the importance of a clear understanding of the value proposition of data and analytics within your organisation. Establishing common definitions and transparent processes amongst teams is a crucial first step towards building trusted data. Enabling conversations between different departments, teams and organisations on differences in business metrics and calculations will promote trust and collaboration, leading to a more data-driven culture. Accomplishing your data and analytics goal requires investment in technology and tools, equally important however is the development of data skills and culture within the organisation.
Operate with a Holistic Approach
To achieve better outcomes from your data and analytics investments, businesses need a holistic approach to their data operations. Break down the operational silos through senior leadership buy-in, promote cross-functional collaboration and training, implement effective and simple data governance practices and policies, and prioritise data quality. Focusing on these activities will create a culture that is skilled in the art of data-driven decision-making.
Build It and They Will Not Come
At Opsworld, a recurring theme was the need to align data and analytics with corporate goals and strategy. Demonstrating the value and tying initiatives to specific business outcomes justifies investments and prevents the “build it and they will not come” scenario, which is sadly all too common in many businesses. Data and Analytics teams must understand the business goals and communicate how data and analytics contribute to achieving them. Identifying and quantifying analytics impact to the business goals secures stakeholder buy-in and creates cultural alignment to the importance of the data and analytics vision. Ultimately, the success of data and analytics initiatives depends on their ability to deliver tangible business value, and this requires a clear understanding of the connection between data, analytics, culture and corporate strategy.
Using Pipelines to Increase Value
Data pipelines are critical for increasing the value of data operations. They provide a framework for capturing, processing, and transforming data from multiple sources into usable insights, standardising and automating the data transformation process. This helps organisations leverage data as a strategic asset, whilst also creating efficiencies and scalability. As data flows through the pipelines, its value increases, making it even more valuable. To build confidence in data-driven decisions, organisations must measure the value brought by data pipelines. Well-designed pipelines increase speed of decision making and enable patterns, trends, and insights otherwise unknown to the organisation to be used to inform strategic decision making. Continually measuring the value of the data pipeline overtime, improves decision making and increases innovation – driving greater value over time.
Data Rich but Data Poor
To reap the full reward from your data it’s not enough to just collect it; organisations must also use it in an effective and efficient way to drive business decisions. A data strategy, tightly coupled to the business strategy, is essential to enabling a positive return-on-investment for data projects and initiatives. More is not usually better in the world of data – more data can increase costs and also risks. Data hacking is a very real and present danger so the focus should be on understanding what data you need to drive your business processes and strategic outcomes and then focus on creating efficient and secure data processes and systems.
Safe Innovation at Enterprise Scale
Innovation is essential for driving business growth, but it must be done safely and at an enterprise scale. To achieve this, organisations need to have effective governance and risk management practices in place to ensure a strong governance oversight. Strong governance requires the Board to take careful consideration of factors such as data privacy, ethical usage principles, and regulatory compliance. Supporting a well governed ‘fail fast and fail small’ innovation culture minimising potential risks whilst enabling organisations to leverage Machine Learning and AI and other emerging technologies to drive business growth and competitive advantage.
The potential of Machine Learning and AI to revolutionise data analytics operations was consistently highlighted, but speakers also emphasised the need to understand their limitations and risks. Many companies are eager to automate data operations using Machine Learning, but they must also plan for the long-term integration of these technologies into their BAU operations.
The buzzword ‘data mesh’ was a popular topic – an approach to data architecture that promotes interoperability and autonomy while enhancing data culture through collaboration. This involves building a network of data products that are owned and managed by cross-functional teams, instead of centralised IT teams, to overcome business silos and promote the use of data for decision-making.
Rising Platform and SaaS Costs
Over the next 12 months, rising platform and SaaS costs will be a significant challenge for organisations seeking to build a data-centric organisation. These costs not only impact the monetary aspects of businesses but also have a significant impact on the environment.
The future in data and analytics is filled with promise. New and exciting tools are coming into the market every day and more and more people are learning skills like data engineering, designing Machine Learning models and storytelling. But with great promise comes great costs, and our time at OpsWorld is a timely reminded that tools alone are not enough to create value from data and analytics. Value is created through the efficient usage of these tools, a strong strategic alignment and a data-driven culture.