August 29, 2021

How to Select the Right Data Analytics Tools & Platforms

Today’s business landscape runs on data. It doesn’t matter if you’re in finance, healthcare, or running an omnichannel retail operation; data powers strategic decision-making, unearths new revenue streams, and reveals hidden waste generators eating into your bottom line.

That said, data alone can’t do any of those things. You’ll need to assemble a tech stack that connects all relevant data sources. Unfortunately, selecting the data analytics tools and platforms that put your business on the right path isn’t easy.

In this article, we discuss evaluation criteria, key features, and more for selecting platforms and tools that fit your business goals.

Choosing the Right Solution for Your Data

Because Big Data applies to such a broad spectrum of use cases, applications, and industries, it’s hard to nail down a definitive list of selection criteria. Here are some suggestions to narrow it down:

  • Identify Goals. While it’s easy to get caught up in the possibilities of Big Data analytic tools, defining the main goals for your program and developing a well-designed strategy is far more important than the tools themselves. What do you hope to achieve with your data strategy? Start by targeting a handful of business problems or opportunities with the biggest impact—be it real-time asset monitoring or a deeper understanding of what your customers want—and build your toolkit around those core goals.
  • Look Toward Industry-Specific Use Cases. Do some research to find out which analytic platforms, tools, and capabilities others in your industry are using to solve problems or create opportunities. For example, retailers might look at how other companies use AI recommendation engines or sentiment analysis to improve the customer experience, whereas a financial services firm may be more concerned with fraud detection.
  • Think About the End-User. To capture the most value from Big Data, you need to implement a strategy that involves everyone in the company, from the C-suite to your customer-facing teams. Consider how analytics applies to different roles within your organization. Which users need simplified solutions to support decision-making? Do you need sales or marketing-specific tools? Do you have data science capabilities?

Types of Data Analytic Tools and Key Features

What are the tools used for Big Data analytics? The tools represent a broad category, though they tend to fall into a few key groups:

Customer Data Platforms (CDPs)

A Customer Data Platform (CDPs), like a Customer Relationship Management (CRM) platform, captures customer data that can be used to improve processes or sell products. However, CDPs take things to the next level.

Where CRMs only collect data from intentional interactions (unless manually entered)—like communications history, website visits, and purchasing behavior—CDPs gather data from anonymous website visitors and manage and track data both online and offline. They handle a variety of data types from a diverse set of data sources.

Core Capabilities:

  • Provides 360-degree view of the customer
  • Connects multiple data sources (1st, 2nd, 3rd-party data)
  • Unifies customer data across all connected systems
  • Improves targeting for marketing campaigns

Business Intelligence (BI) Tools

Today’s business intelligence (BI) tools help businesses see and understand data. According to Gartner, BI tools span three main categories:

  • Online analytical processing, or OLAP, enables data discovery, ad-hoc reporting, simulation models, performance management, and other complex analysis capabilities.
  • Information delivery serves up insights in the form of visualizations, reports, and dashboards.
  • BI integration deals with metadata management and provides a development environment to support your strategy.

While these platforms are pretty diverse, the goal with BI tools is to help organizations become data-driven through processes like data mining, predictive modeling, and natural language processing. We’re also beginning to see trends like embedded analytics and data visualization capabilities that give non-technical users access to insights that once required IT assistance.

These accessible platforms allow citizen data scientists to apply their knowledge of data, analytics, and the business to answer critical new business questions.

Core Capabilities:

  • Data visualization
  • Predictive modeling
  • Data mining
  • Forecasting
  • Automated reporting
  • Customizable dashboards
  • Integrations with other data sources and platforms
  • Data quality management
  • Natural language processing (NLP)
  • Performance management
  • Ad-hoc analysis
  • Simulation models
  • Budgeting

Customer Analytic Tools

Customer analytic tools are designed to manage the full analytics process—from preparation to insight generation. In most cases, customer analytic platforms come with pre-built data models for forecasting, propensity to buy, and a variety of statistical analysis techniques to understand customer behavior and optimize products, services, and experiences.

Keep in mind, customer analytics platforms are more sophisticated than the tools your marketing or sales team might use. While pre-built models make these platforms more accessible to non-technical users, you still need advanced data science skills to develop and run custom models or gain total visibility into the customer journey.

Core Capabilities:

  • Granular segmentation
  • Customer satisfaction insights
  • Statistical modeling
  • Acquisition, retention, and churn metrics
  • Text analytics
  • Extendable custom models built in R, Python or SAS

Digital Experience Platforms (DXPs)

Digital Experience Platforms (DXP) are a relatively new type of enterprise-grade software designed to optimize the customer experience at every touchpoint. While DXPs overlap with customer experience management (CXM) platforms, DXPs focus more on streamlining processes and coordinating and personalizing content to users across a wide range of channels—including the Internet of Things (IoT), digital assistants, and virtual reality experiences. Here, the main goal is to provide marketers with strategic control over branding and content presentation.

Core Capabilities:

  • API-first architecture
  • Multi-touchpoint management
  • Dynamic templates for automating personalization
  • Content management and delivery

Features to Look for in a Data Analytics Platform

According to a report from BI Survey, leading companies are more likely to build custom solutions or invest in individual tools that match the desired capabilities than laggards, who are more likely to go for a full-stack platform. All-in-one options may be a better bet for organizations with generic use-cases and a limited amount of data projects, as well as those looking to implement and train quickly.

It’s also worth noting that the make vs. buy question needs to be considered at every layer in your stack. That means you need to look at data processing, AI, machine learning, storage, predictive models, and integration platforms individually to make sure you cover all of your bases.

Professional Services and Support

What do you want from a service provider? Some solutions (SaaS products in particular) are based on business models that benefit from being helpful (customer retention). Consider whether you would like to work with a partner that offers hands-on support and guidance around getting the most value from their solution.

Other solutions—like open-source platforms (Hadoop, Spark, Apache) and self-hosted solutions—tend to be more self-guided. Here, you purchase from a vendor and look toward internal expertise, FAQs, and how-to documentation to guide the process. Keep in mind that there may be a trade-off. Where SaaS tools often come with much more support, they have some limitations and might not support every data set or integration you would like to use in your tech stack.

Data Storage Options: SaaS vs. Self-Hosting

Choosing between a SaaS solution and hosting on-premises is another key consideration. SaaS solutions are cloud-based and managed by a third-party provider that shoulders the burden of managing a platform’s IT infrastructure, which includes storage, security, and data backup.

SaaS hosting is a cost-effective, scalable solution that can reduce time-to-market and integrate with other systems. The benefit of using a SaaS tool for BI or customer analytics is that it’s easier for non-technical users to access and understand insights without help from an analyst or IT.

By contrast, self-hosting means you’re running a platform on your own server. While there’s more work involved with getting started, self-hosting enables more customization than SaaS platforms. For larger organizations requiring custom options, a self-hosted solution is likely a better bet, as you have more control over data integration, reporting tools, and automation designed around your unique needs.

Key considerations:

  • What business needs are you trying to address?
  • How much data will you need to process for this project?
  • Do you have existing hardware that can handle the data requirements?
  • Can you easily scale up to accommodate growing data sets?
  • What is the current cost of data, including hosting fees, IT infrastructure, maintenance, and other internal resources?
  • Who is using the data, and how will they be accessing that information?

A quick note: SaaS analytics tools range considerably. On one end, you have tools like Microsoft Azure and Sisense, which support real-time data streaming and provide self-serve analytics—though you need to do some coding to gather advanced insights. On the other end, you will find more accessible solutions like HubSpot, Marketo, or ActiveDemand, which are designed for marketing and sales teams with little coding or data science experience.

Data Reporting and Presentation

According to Harvard Business School, Big Data isn’t typically used in a way that maximizes value. Companies are better at collecting data about everything from customers to competitors, yet they fall short when it comes to analyzing insights and applying them strategically.

With that in mind, consider who will use these tools and what they will be using them for. Note whether or not they have a data science background or need a simplified reporting tool that quickly presents information so that they can apply it to other activities. For example, if you’re looking at solutions designed to help sales teams make more informed decisions, you need something that enables them to access key insights quickly as they engage with buyers.

Key Considerations:

  • Will results need to be displayed quickly or in real-time?
  • Will you need a solution that makes data easier to understand?
  • Or are you looking for a tool that only analysts will be using?
  • Do you need custom reporting capabilities?

Ongoing and Future Costs

Several factors contribute to the overall cost of your solution:

  • Amount of data
  • Data complexity and cleanliness
  • If you’re using AI or machine learning applications
  • Data science IT capabilities
  • Number of use-cases
  • Whether you are self-hosting or working with a third-party provider
  • If the solution provides cost-effective scalability

Data Integration

Big Data integration platforms help companies tear down silos across their entire data ecosystem. They’re a critical tool for managing and storing data clusters, particularly when you’re dealing with data captured from a wide range of IoT endpoints, applications, and data types. In some cases, data integration platforms offer stream analytics capabilities, but they’re generally better suited for data management.

Need Help Selecting the Right Data Analytics Tools?

We’re really just scratching the surface when it comes to data analytics platforms. However, the main takeaway here is that you should have a clear business case and strategy in place before investing in any solutions.

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