Getting more value out of data was considered the number one digital initiative for businesses in the short term, according to Nimbus Ninety’s 2021 Digital Trends Report. Yet, a recent Deloitte survey discovered that 63% of business leaders say that their companies are not yet “analytics-driven”. So, how can organisations cross the gap from aspiration to action?

With artificial intelligence and machine learning becoming the buzzwords of corporate strategy, the journey to advanced analytics is simultaneously an exciting and intimidating one. The road requires the vehicle of a solid data foundation, with all members of an organisation taking on the responsibility of the driver. But how do organisations kick-start the ignition? We sat down with IT, data and business intelligence leaders to explore how to build a solid data strategy that drives data literacy and progressively builds analytic capabilities amongst the user base. Take a look below for the blueprint that takes the bumps out of the road on the journey to a mature and lasting data-driven culture.

Choosing the Right Parts: Building A Future-Proof Analytics Culture

Pyramid Analytics established the three core principle foundations of successfully building a future-proof analytics culture:

1. Software solutions need to be simple and highly adaptable. Analytics software needs to be easy to use. This will facilitate an organisation-wide adoption of the platform and allow for speedy time to value. Software with an intuitive user interface and drag-and-drop functionality makes for an easy onboarding process for employees. It is, therefore, best to use a simple and highly adaptable code-free interface that can match the user’s functional needs, requirements, and skill level.
   
   
2. Organisations should select a solution that can strategically meet their business goals. The analytics solution should be strategic; with its tasks and results used as stepping stones to reach business goals. Software should not, therefore, not be considered a cost, but an investment into the business. Leaders should be able to prove the ROI at multiple levels of the organisation. The strongest business cases are when the business leaders pair up with the IT to produce business value, rather than employing a shadow IT solution that is distant from the central function.
   
   
3. The solution needs to be shareable and accessible to the entire organisation. The platform needs to future-proof an organisation’s analytics strategy and must be accessible to different roles within the organisation. It should generate insights and value at all levels, but in particular, at a leadership level. Managers and C-level executives need to understand the business value of the technology. Therefore, the data output should be presented in a format that allows them to understand how it can add value, and how it can be used in corporate strategy.

The Three Stepping Stones on the Data Brickroad: Laying Successful Analytic Foundations

Marie Hense, Data & Product Strategy Director at Lumina Intelligence, set out the steps to create a solid analytics foundation to truly utilise the power of AI and ML. AI and ML algorithms are only as stable as the data foundation, so it is paramount to structure the data. Different data users have different needs, so presenting the data in the right data format for every data user, from the data scientist to the CEO, will drive data literacy. The top three takeaways for laying the foundation for a successful analytic foundation were identified as such:

1. An AI and ML strategy starts with sound data foundations. It is important to not cut out the organisation stage and ensure a stable data foundation in order to truly harness the power of AI and ML.
2. An agile approach is really important when it comes to data building. This approach ensures an organisation’s time and budget is invested wisely.
3. The right tools make all the difference. The right user experience and the flexibility of the platform are key considerations to ensure its adoption.

Roadblocks and Guiding Lights on the Journey to Advanced Analytics

We then sat down and took a deep dive into our IT and business intelligence leaders data strategies. Key takeaways of the challenges and lessons learnt were:

• It is important to set AI and ML expectations. IT and business leaders need to set the expectations within the business of what AI can actually deliver. This means illustrating both the business and individual value, but at the same time, not buying into the hype that often surrounds these technologies. Organisations need to question whether the answer always is necessarily AI or ML; and when it is, ensure that they illustrate the value without presenting it as the magic solution to all business needs.
  
  
• Organisations need to teach their users how to self-serve. In order to be less reliant on the data teams and cultivate a long-lasting data-driven culture, organisations need to train their teams to overcome the data fear. This can be done through education with the likes of training videos to ease the often difficult transition of adoption from the non-data savvy.
  
  
• Remember to take it right back to the beginning. It is important to get your data foundation in place before starting the advanced analytics journey. This means conducting data audits of various departments, understanding what data they have, where it is, and how it is used. This helps to identify potential use cases further down the line as it can be identified how AI and ML can match and fill potential data gaps.
  
  
• It is important not to dive straight in. Organisations should decide prior to obtaining a software solution what exactly the value is that they want to gain. After establishing the solid data foundations, use cases should be identified. Often, analytic insights generate more questions than you started with, so it is important to understand how insights will be applied.
  
  
• It is complicated keeping things simple. Participants emphasised the importance of scalability, flexibility, and useability of solutions. It is important to keep things simple so all levels of an organisation can gain valuable insights. Data skills are in short supply, so keeping things simple is a key criterion for cultivating an analytics culture.

This event was in partnership with Pyramid Analytics, a business intelligence software company