You can’t just stare down the path of data and analytics modernization. You need to take a balanced approach and have a strategy in place in order to migrate to a scalable, agile, and future ready data and analytics ecosystem.

Data and analytics modernization requires more than simply adopting new technology. As organizations begin their path to modernizing their data architecture and analytics capabilities, they need to take a balanced approach that factors in everything from the tools and technology they are using to the processes they have in place, and the people charged with using and understanding both.

Tony Dahlager, VP of Data Management, and Kevin Lobo, VP of Analytics at Analytics8, recently presented the case for how to build modern data and analytics solutions during “The Path to Data and Analytics Modernization” webinar. At the end of the webinar, attendees asked questions about creating a data strategy around modernization, setting up the appropriate data architecture, and getting buy-in from the c-suite. In this blog, we’ll go over all the questions and provide detailed answers for each.

Question 1: Where within the business do you start a data and analytics modernization project? Should you take a siloed approach around each business function, or is a 360-approach better?

Answer: We see organizations doing both—taking it one business function at a time or looking at it holistically. However, having a 360-degree approach enables longer term success by prioritizing a holistic data foundation, as opposed to starting with a narrow focus on one business function and attempting to scale outward.

Think of it through the same lens of planning a road trip: You begin with mapping out your starting point and end point destination. You have a rough approximation of how long it will take to get there and the route you’re going to take. Your route may change as you move along, but you adjust along the way to account for any changes in your plan. That’s the same as a 360 approach. What you don’t do is route incrementally 10 miles at a time and constantly adjust without knowing where you’re headed. That’s the same as a siloed business function approach.

There is risk with just looking at each individual business function as a starting point. Whatever you end up selecting as your solution for data analytics may not scale well or adapt well to the other requirements that you have across the business. An iterative approach, however, allows for checkpoints to celebrate milestones, learn from the work that’s been completed, and to adjust prioritizations accordingly so that you can apply lessons learned to other business functions. A 360-strategy is a great way to make sure that you’re capturing all the needs that your business has today, and that you’re picking the people, processes, technology, and the data that you need to be successful, holistically.

Question 2: How often should you revisit your data and analytics strategy? What are best practices to follow?

Answer: It is difficult to build roadmaps for analytics that span longer than 12 to 18 months, so it’s typically a good practice to revisit your data and analytics strategy annually. Within a year, you can also make great strides building out your capabilities and becoming more analytically mature, so this will give you an opportunity to re-examine business objectives. We commonly see that needs evolve as business sponsors get more familiar and more confident working with data.

Other indications for a need to revisit the data strategy include implementing a new source system within the organization, acquiring or merging with another company, or bringing in new leadership with a new perspective on data or in changing the strategic focus of the organization.

Question 3: If all you’ve got in place is Excel reporting, where do you start with modernizing your data and analytics? How do you know if you need a consultant to help?

Answer: It starts with understanding the needs of the organization. If there is a possibility that the organization can benefit from a more advanced use of analytics than what Excel allows, it’s a good idea to start building an approach to meeting those more advanced needs. Excel is a very common tool with an important place in the enterprise. But it is not a strategic solution for an organization that needs to make intentional, data-driven decisions to get insights into aspects of their business-like growth, customer service, and workplace culture.

If you’re unsure how to start the process of moving away from Excel, performing a data and analytics assessment can help you approach the modernization effort in a strategic and well thought out way. In an assessment, you start by defining your business requirements, data and analytics needs, and then developing a path for how to migrate to a more modern BI tool and advance in analytical maturity. During an assessment, focus a lot of time purely on your analytics needs. Think about what your priority use cases and requirements are. If your organizational priority is Sales Reporting, then what are your core KPIs, the types of reports you’d like to see, and feature functionality in a future state tool? What are the data sources, and how will they come together?

After you take stock of your analytics needs, you can start to evaluate your data processes—what is working and what isn’t. Find manual processes and look for opportunities for automation to reduce risk and error. You should also look to identify gaps that exist in your current processes and attempt to remediate them. For example, if your focus is on order fulfillment reporting, do you have transportation data available to show on-time delivery? If not, look for ways to fill that gap.

As to whether you need outside consultant help: the first step is to evaluate the data literacy and skills gaps within your organization. Are your workers on staff equipped and have the requisite background to handle the learning curve that comes with implementing a new analytics tool? If not, then contracting outside consultant help initially may be a good to start.

Question 4: What architecture works best for real-time reporting (data lake versus data warehouse)?

Answer: What your architecture looks like will depend on how complex your business requirements are. From a conceptual perspective, you will need to build a pipeline that bypasses a more traditional nightly ETL framework to allow you to do real-time analytics. For some organizations, that’s going to be as simple as building a data replication layer using change data capture (CDC), either in a data warehouse or a data lake, where you can then virtualize whatever modelling you’re doing for analytics using views to ensure that data is up to date when queried. Other organizations with significant data volumes are going to need more complex solutions such as data streaming.

Question 5: I know our organization needs to modernize, but how do I get buy-in from the c-suite?

Answer: You need to start by building the business case for investing in data and analytics. Identifying key impactful use cases that will yield quick and obvious results is a great place to start. Highlight potential quick wins and easily quantifiable value. Rather than positioning a widescale migration of existing legacy environments, start with new or emergent use cases that are feasible and can bring value quickly to the organization.

Help them understand the current limitations—and how those limitations affect the profitability of the company. Often, the current-state pain points are a compelling case for change.

Looking into the future, executives will want to know the hard benefits to the business, the level of cost any expected investment will require, and what new risk a solution may introduce. Make sure not to over-promise since the quickest way to spoil your authority is by presenting numbers that are easily debunked.

Finally, consider approaching the executives with options that are supported by both technical and business facing sponsors. An eventual solution is likely to require partnership across the organization, so it is effective to introduce the initiative in this same way.

Sharon Rehana Sharon Rehana is the content manager at Analytics8 with experience in creating content across multiple industries. She found a home in data and analytics because that’s where storytelling always begins.
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