Cutting Through the Noise: The Real Deal on Modernizing Analytics and Reporting

The data and analytics market is constantly being flooded with new technologies and innovations. As a result, companies feel pressured to move away from legacy systems and leverage newer, better analytics capabilities.

But how do you estimate the ROI on this investment? How do you cut through the noise and select modern analytics tools that will work best for your needs and avoid getting sucked into trendy options that won’t grow with the business? And once you decide, how do you initiate the modernization process with minimal disruption to normal business operations?

Why You Need to Modernize Your Analytics and Reporting

Analytics modernization is necessary if you truly to want to harness data as an asset.

As the data you collect—and sources from which you get data—continues to grow, analysis becomes more difficult. While data presents greater opportunities to innovate and grow your business, its value only comes from being able to draw actionable insights. When data becomes more complex, so does making sense of it all.

What is analytics modernization?

Fundamentally, when you modernize your analytics, you get an agile analytics solution that scales with growing data demands and offers greater analytical functionality. With modern analytics, you bypass the traditional effort and costs of retooling and adding integrations each time you want to add a new data source or do more advanced analysis.

Often characterized by being in the cloud, modern analytics solutions make it cheaper and quicker to blend your data in a single place for analysis; but advantages go beyond that. These solutions expose advanced analytical capabilities that help you make smarter decisions.

The Benefits of a Modern Analytics Solution

1.) Real Time Analysis

With rapidly changing business landscapes and evolving customer demands, making decisions based on relevant, real-time data can make or break a business.

Through enhanced features like native API integrations, direct querying on the data source itself, and increased refresh intervals, modern analytics provide the capabilities to integrate real-time or streaming data sets as opposed to being tethered to a static refresh schedule. Analysts can access up-to-date information for forward-looking decision making.

2.) Embedded Analytics

Embedded analytics is integrating analytics directly into applications, operational systems, products, or customer portals which allows users to view and analyze their data within the native application, without having to toggle into a separate platform or dashboard. This improves productivity, encourages user adoption of analytics tools, and increases end user satisfaction (which is especially important if you are trying to monetize your data and analytics). Embedded analytics is a great solution for complex use cases where the BI tool itself cannot meet every requirement (for example- writebacks or enhanced collaboration).

This can sometimes be accomplished with a legacy solution through an iFrame, but modern solutions make embedded analytics much simpler. Most next generation analytics tools are expanding embedded analytics capabilities, providing super easy ways to embed analytics like through expanded API integrations).

3.) Better Analytics, Including Built-in AI Capabilities

With traditional platforms, you are often tied to charts, tables, and visualizations that provide descriptive analytics (what happened). Modern analytics platforms allow you to elevate your analytics because of built-in, easy to use AI features that are fully integrated into the front end.

Machine learning capabilities, customizable R/Python visuals, NLP functionality, and decision tree-based visuals are the types of native features found in modern analytics solutions which allow for powerful use cases for data science.

Machine learning models that live outside of a legacy analytics tools can now be integrated directly into your dashboards to turn your descriptive analytics into prescriptive analytics.

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4.) Greater Collaboration

Traditional analytics platforms typically serve as an entry point to reporting, while modern analytics platforms operate on the principle that insights can only be impactful when shared and distributed to a wider audience, in and outside of the organization.

Modern analytics tools come equipped with the ability to set up report subscriptions, KPI alerts, collaborative comments, and easy report sharing outside of the tool.

The easier it is to collaborate on your analytics platform, the more people/departments across the organization you’ll have invested in data-driven decision making.

5.) Enhanced Data Prep and Deployment Capabilities

Legacy tools are black box in nature; it’s difficult to see what’s going on inside semantic layers. Next generation tools allow for full transparency in the data transformation process. Data lineage schemas allow developers to see where data is being sourced from and how it flows into the end state application. Some next generation solutions even have deployment pipelines to govern the promotion process from Dev, Test and into Production. This type of built-in functionality lets you see where your data is coming from and what/if any transformation steps are being applied—allowing you to democratize your data and build a new level of transparency.

Further, traditional tools take a “one size fits all” approach to deployment which means you’re tied to a specific framework from which to build your applications. But with modern tools, you choose the approach that best suits your immediate and long-term needs—build a data model inside the tool itself, run queries off a live production data base, or take a combination approach. You also have the option to deploy in the cloud, on-premise, or as a fully hosted SaaS solution.

This flexibility on the back end opens up so many possibilities for the data you can present on the front end.

How To Get Started – Tips for Modernizing Your Analytics Environment

1.) Pick Your Modern Analytics Tool

One of the biggest mistakes I see organizations make on a modernization project is going through a rigorous tool selection process only to choose a solution because it’s the most aesthetically pleasing, or the price point is too good to pass up.

It’s critical to remember your analytics solution isn’t simply a standalone tool, but part of an end-to-end modern data stack (which should be defined in your data strategy).

Locking yourself into an analytics solution that isn’t a natural fit into your data architecture sets up significant development hurdles before you even start the modernization process. So if you’re building your modern data stack on a cloud platform like Azure, AWS, GCP or Snowflake, then you should consider how each analytics tool (Power BI, Qlik, Looker, Tableau) fits into these respective architectures.

We often manage bakeoff processes and pilot projects with our clients to help them cut through the noise and determine which tool best accommodates their data and organizational goals. In a bakeoff process, strengths and weaknesses of each option are explored, and scoring is conducted with battle cards. Four-week pilot projects utilizing a sample of real data help prove out functionality of analytics tools.

Approach the modernization process like you’re purchasing a car. You wouldn’t buy a car without taking it for a test drive, and you should approach your analytics selection process in the same manner.

2.) Assess Skill Sets

Often the analytics selection process takes on such precedence that the question of “who” gets obscured. Your people must be factored into decision-making because they will ultimately be responsible for building and developing reports.

A modernization project isn’t just a shift in technology; it’s also a shift in the skillsets required within your organization. If your team of developers must pivot from their legacy platform to a modern analytics solution, they need an enablement plan to mitigate the learning curve.

Consider how they’ll receive training too—can you develop a strong training program internally, or do you require outside help initiate?

3.) Narrow Your Focus on the Initial Build Phase

Modernization for many companies often means there is years of analytics reporting that needs to be migrated to a new tool. A migration strategy will help you overcome this daunting task by narrowing your focus on the short term: what will be migrated initially and which apps and reports can be retired.

Select high value reports and applications for the initial migration to organically build adoption for your new analytics solution. Widely consumed, highly visible reports in your organization are great candidates to migrate first.

4.) Have a Roll-Out Plan in Place

When it comes to reporting, the modernization effort is as much educational as it is technical. In order to encourage adoption, a roll-out and training plan must be in place to educate end users on the feature functionality of your new analytics tool. Consider scheduling a one-day bootcamp training with your end users to educate them on their new analytics tool.

Users will only adopt a tool if they’re comfortable using it, so the more you prepare and educate your user base, the better chance of success you’ll have in your modernization initiative.

Schedule a Data Strategy Session

Modernizing your analytics is not a single action or tool. It involves rethinking how you use analytics as a company. Modern analytics solutions offer new and innovative means to drive efficiencies and excel with data, and now is the time to take advantage of this.