Tales from the Field: Best Practices for Data and Analytics Initiatives
In 2022, the Apps Associates Data and Analytics teams helped many clients define and implement strategies and approaches to improve Data and Analytics at their companies.? Some clients we helped were in the very early stages of defining their Data and Analytics strategy and some were a bit further along.? Regardless, some common themes and best practices emerged and were reinforced throughout the year for successfully defining and executing Data and Analytics initiatives.
1. Solve a business problem(s) - at this point, it is common knowledge that an organization’s data can be used to enable a variety of positive business outcomes.? Armed with that general knowledge, some organizations decide to pursue Data and Analytics initiatives without being specific enough in defining the outcomes that they want to achieve.? We recommend specifically identifying the outcomes that should be achieved by the Data and Analytics initiatives and to tie those outcomes to the organization’s overall strategic plan and objectives.? Typically the desired outcomes for Data and Analytics initiatives fall into one or more of the categories below:
2. Start small and scale up - nothing is more likely to derail a Data and Analytics improvement program than trying to achieve too much in the initial phase.? If the initial phase fails, then senior management will often conclude that it is simply too costly and risky to attempt to make any real improvement and the program is often scrapped.? The company then settles for very minor improvements around the edges and runs the risk of falling behind its competitors.? Conversely, achieving success with the initial phase of a Data and Analytics improvement program has the opposite effect.? Senior Management and end users will want additional improvements and will be confident that it is possible to achieve them - so funding of the next set of initiatives will be easier to obtain.? Achieving success with the initial phase sets up a beneficial, repeating cycle of success and subsequent additional funding.
3. Understand your data and data sources - needless to say, data is central to achieving success with a Data and Analytics improvement program.? In today’s world, the data of an organization exists in a variety of application systems and databases that span on-premises and the Cloud.? To assess degree of complexity, level of effort and make decisions about phasing it is important to understand the following information about your data and data sources:
4. Know your audience - Data and Analytics platforms are unlike application systems in that oftentimes they don’t have to be used.? For example, if you implement a new Enterprise Resource Planning (ERP) system and retire the old one, then the users of the old ERP system have no choice but to use the new ERP system.? With Data and Analytics it is different.? The end users can choose not to use the new platform and set of tools or to use them in a very minimal fashion.? The end users can typically find ways to ‘work around’ the new platform if they don’t like it or it does not fit into the way they do their jobs. They can find someone to generate a file for them or they will only use the new Data and Analytics platform as an expensive mechanism for downloading data. ? It is therefore necessary to understand who your audience is, what their preferences are in terms of receiving and interacting with data and what KPI’s and metrics are important to them.
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5. Embrace the Cloud - It may not be necessary to include this as a best practice because Cloud adoption has been accelerating and the vast majority of our clients want to build out their new Data and Analytics platform in the Cloud.? However, we feel it is necessary to mention this because the benefits of the Cloud so far outweigh on-premises in terms of scalability, efficiency, agility, flexibility and cost effectiveness. ? We feel it is important that there be no lack of clarity on this topic - Cloud and on-premises are dramatically different when building out an enterprise Data and Analytics platform.
6. Evaluate Cloud-based technology platform alternatives - If you have not already selected a Cloud-based platform for your Data and Analytics improvement program, it is advisable to evaluate the different alternatives.? At minimum you will need to review alternatives for the following essential components of your Cloud-based Data and Analytics environment:
For the data platform itself, the most popular platforms are (in no particular order): Snowflake, Azure Synapse, Amazon Redshift, Oracle Autonomous Data Warehouse, Databricks, Google Big Query.? For reporting and analytics there are many quality platforms.? Some of the most popular are (in no particular order): Oracle Analytics Cloud, Microsoft Power BI, Tableau, Microstrategy, Looker, Thoughtspot.? When evaluating alternatives, it is important to factor in not just functionality but also future scalability, cost and ongoing support needs. ? ? ? ? ? ? ? ? ? ?
7. Include Change Management and Training - the success or failure of a Data and Analytics improvement program is highly dependent upon the ability of the end users to understand the platform and to use it to extract value and discover new insights.? If the end users don’t understand how to use the platform they typically ignore it and find other, less efficient ways to get the data that they need.? It is important to include formal Change Management and training activities in the initiative to make sure the end users are adequately prepared to leverage the new platform and set of capabilities.
In today’s business landscape it is necessary for companies to leverage their data to remain competitive and not fall behind.? However, it is quite possible to spend money and not get the intended results.? As these best practices outline, a holistic approach, as opposed to a narrow focus on technology platforms, will yield the best results.? We hope your Data and Analytics initiatives in 2023 are extremely successful.