If 2018 taught me anything about data and analytics, it’s these 3 things
As we close out Talavant’s 4th year in business, I want to look back at what we’ve accomplished, what I saw in data and analytics and where I think things should lead in 2019.
2018 was another fantastic growth year for Talavant. We opened a new office in Milwaukee, and expanded our staff by 50% and our projects by 75%. Madison Magazine also recognized us as one of Madison’s best places to work. We strive to create a learning and growth culture, and the recognition feels fantastic. We are continuing to build our reputation in the data and analytics space and gain strength with every new hire.
From a client perspective, I think there were 3 key trends that I saw in 2018:
1. Broad adoption of self-service Business Intelligence (BI) tools
2. Significantly more trust in moving data workloads and storage to the cloud
3. Lots of interest in Machine Learning and Artificial Intelligence (AI)
Adoption of Self-Service BI Tools
Companies have really started to fall in love with self-service BI tools. Specifically, the broad adoption of Microsoft® Power BI and Tableau®, two of the leading BI tools in this space. Both tools enable end user friendly analytics and visualization. Organizations have found a great way to turn data into value without having to always get help from IT.
This value comes with a bit ( or sometimes a lot) of cost. As business users create more and more content, their focus is not always centered on reuse and can often be siloed; this problem has existed long before self-service BI tools. People naturally gravitate towards what serves their need, and it’s understandable why the proliferation of this data is so pronounced. However, without a solid data governance structure, data will be mislabeled, inaccurate and in the worst cases completely untrusted by end consumers.
I believe 2019 will see a resurgence in data governance programs. Companies will shift the pendulum back from unfettered data access to a controlled environment where IT has more input in how data is collected, defined and delivered to consumers. Successful organizations will implement programs that find a balance between exploratory self-service BI and the creation of trusted data sources and defined metric definitions.
Movement to the Cloud
The movement to the cloud has been happening for some time, which is evident in the considerable growth of Microsoft®, Amazon® and other cloud platform providers. We’ve seen entire data centers being pulled out with a small number of servers left behind.
Organizations were often a bit hesitant to move data workloads entirely to the cloud. They had too much investment in existing infrastructure and were concerned about security. As infrastructure has aged out, companies are taking a much harder look at moving workloads and platform providers have gone to great lengths to tackle security concerns.
From a data and analytics perspective, cloud platforms have opened the door for a host of new opportunities. In the past, taking the leap to build out large data warehouse infrastructures was sometimes difficult to justify. Companies would often save capital by keeping only minimal environments and would, therefore, have terrible performance or dev ops. Being able to purchase platform services on an as-needed basis allows companies to jump into Big Data technologies or stand up Massively Parallel Processing (MPP) databases without making a substantial up-front investment in hardware and all its related costs.
Going forward, we will see more creativity in the types of data and analytics infrastructures being built as companies continue leveraging cloud technologies. Less reliance on just moving data into a database and more openness to coordinating the right services for the right job, such as moving flat files into blob storage, processing large volumes using tools like Spark or Snowflake, and building out department-specific sandboxes using Azure SQL or My SQL. Right-sizing solutions with the right technologies is now more available than ever.
Machine Learning and AI
Machine learning and AI have to be the buzzwords of the year. Like Big Data 5+ years ago, everyone is talking about Machine Learning and AI. These are certainly promising technologies in the data and analytics space, and companies with strong data infrastructures should be able to capitalize on predictive analytics and improved identification of trends that would have been impossible for an analyst to see before these technologies were available.
The key in 2019 for companies wanting to leverage Machine Learning and AI goes back to implementing a robust data infrastructure including good data governance practices. Companies need to invest in defining their core data assets and building comprehensive processes to mine and refine their data. If they don’t, the same IT adage that applied in the past will continue – Garbage In, Garbage Out.
Moving Forward in 2019
Businesses looking to capitalize on their data assets in 2019 need to assess their current scenario and focus on the following:
1. Have we identified from the business those key metrics and supporting data that drive critical business decisions?
2. Do we have the processes and infrastructure in place to effectively mine and refine the data assets we have?
3. Do we have the skills necessary to identify these priorities and make the most of our data?
Working with an experienced BI team can help your company not only answer these questions, but design and deploy solutions that will help you make smart business decisions. If you’re ready to make the most of your data, 2019 is a great year to start.