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IT-Managed Analytics: Pros and Cons

Scott Hietpas Scott Hietpas  |  
Jul 30, 2019
 
In this blog series, Scott Hietpas, a principal consultant with Skyline Technologies’ data team, explores the advantages and disadvantages of different analytics management approaches. For a full overview on this topic, check out the original IT-Managed vs Self-Service Analytics Webinar.
 
Before we can talk about IT-Managed vs. Self-Service analytics, it’s important to get context around what a typical solution looks like.
 
First, you start with various source systems like an ERP, CRM, or even log and marketing files. Ultimately, we want to ingest that data into some storage like a data lake. Then we might move that data into a data warehouse. In the diagram below (which may not be exactly what your solution looks like but does capture a lot of the key elements of the process), we see that data moves from Azure Blob Storage to an Azure SQL data warehouse with Azure Analysis Services on top of that.
 
modern data warehouse
 
Azure Analysis Services add an optimized semantic layer that can make data easier to consume in a rich data visualization tool like Power BI. We may also have data science and other use cases that are taking advantage of the data lake. On top of the data lake, we might move data into Azure Cosmos DB to have performant access by other applications.
 

Pros of IT-Managed Analytics

Many layered components can go into a modern data warehouse and full data architecture solution, and each component serves a very distinct purpose. The scope of this blog series is not to dig into the details of this particular architecture (because we did so in a previous blog series), but we need to be aware of these factors because they play into the role of an IT-Managed solution and give us some considerations into how to handle Self-Service.
 

Single Source of Truth

The reason we have a lot of those architectural components (or layers) is so we can take all the complexity across those source systems (ERP, CRM, etc.) and give the business a clear, concise, and single source of truth. We bring all that data together to provide the answers the business is looking for. It provides centralized and standardized data across the organization.
 

Quality Controls

Now, when we bring that data in, inherently there can be some data quality issues. IT-Managed processes are set up to conform data, identify quality issues, and ultimately resolve or flag those to be addressed. Your IT team can do data validation, testing, performance-tuning, and create quality controls.
 

Highly Skilled Team

The work of an IT-Managed solution requires a highly skilled team: typically an IT team with formal training and a process around tools, version control, and collaboration. Therefore, these teams generally will apply best practices that have evolved over years (or even decades) to deliver the best solution.
 

Cons of an IT-Managed Solution

Overall, IT-Managed works very well in meeting business needs, but there are some downsides.
 

Scalability

Recently, the demand for data (and the demand to analyze and get insights into that data) has grown. It's challenging for IT to keep up with the demands of the business. We might be able to scale up by adding additional members to our IT team, but we really can't scale to the same degree that the business demand is growing.
 

Perception

There can often be a perception problem. If we're funneling all data analytics needs through IT, we must prioritize which needs get met first. Consequently, some business areas may feel underserved. There may be a perception that IT can't meet their needs, or at least can’t meet them in in a timely manner.
 

Lack of Innovation

IT may be providing some guidance along what might be good for the business, but business users ultimately aren't able to run with their own ideas to get the data insights they want. That can lead to a lack of on-the-ground innovation.
 

Mitigating the Risks of IT-Managed Analytics

As a result of some of these challenges, Self-Service has become an option. But there are some ways to help mitigate the risks of an IT-Managed solution or an IT-Managed and Self-Service combination.
 

Transparency

One of the biggest challenges around an IT-Managed solution is latency: the time it takes for IT to go through that more rigid or disciplined process to bring data into a single-source of truth model. If the business has transparency into that process, and various stakeholders understand the priorities that are being developed, there can be a lot more acceptance. When they have visibility to the backlog and are aligned on priorities across the organization, then stakeholders can see why that latency exists and can see the benefit to it.
 

Demo/Review

Along those same lines, part of seeing the benefit is being transparent about the outcomes that you're producing. That means having demos or reviews of the work that you're completing. Often, IT might get requests that other users don't have visibility into, and those users are not necessarily seeing what IT is producing. Having monthly or quarterly demos to showcase all the value-add that the IT team is bringing to the organization can really help improve perception.
 

Monitoring

A common complaint with IT-Managed is that, once we start to see rapid growth in usage, there might be complaints that things are slow or we're not meeting a service level agreement. Having monitoring around your key systems and processes to quantify whether you truly are meeting those service level agreements can be impactful.
 

Guidelines

Finally, the key differentiator between IT-Managed and Self-Service analytics is having that discipline of well-defined processes. It's important to make sure your best practices are understood and documented so they're being followed across the team.
 

Conclusion

The IT-Managed analytics solution offers plenty of advantages, but also disadvantages that can only be partially mitigated. This is where Self-Service has come into play. An IT-Managed solution will be vital for some organizations but may not fit the needs of others. In my next blog, I’m going to look at the pros and cons of the Self-Service analytics framework.
 
Analytics

 

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