What is a Data Analytics Platform?

A data analytics platform is an integrated suite of tools and products that provides the capabilities for data analytics. These capabilities include extracting data from data sources, cleansing data, combining data, calculating metrics, analyzing, monitoring, error reporting, data reporting, and data sharing.

In this article, we will explore different types of data analytics platforms, some of the benefits, and how to choose among products with different strengths and focuses. 

The most important takeaway, however, is that a strong data analytics platform in your company is not the result of finding and purchasing the perfect tool or set of tools. Creating a solution that involves the right tools and processes is the most effective way to create a data analytics platform for your company.

Types of Data Analytics Platforms

Many companies build a general-purpose solution for their data analytics needs and goals. Some focus on being world-class at certain types of analytics. And some companies create internal data analytics platforms that fall under more than one of the categories below.

App Analytics Platform

Understand how your application is behaving, trouble points, and how users are using it. This a category that has many SaaS solutions that can give insights into product analytics. However, companies that want to combine product usage analytics with customer service data, sales data, or other data from their company must collect and combine data from all these sources.

An app analytics platform is good for customer-centric reporting and understanding.  

Cloud Analytics Platform

Get all your data into one place in the cloud. With a cloud analytics platform, your data is centralized and available to the people who need it.

Cloud-based analytics are more scalable, easier to maintain, and usually more cost effective than on-premise servers. 

Be sure to find providers with strong security practices to be your foundation for data storage, movement, and analysis.

Digital Experience Analytics Platform

See how your prospects and customers are interacting with your system. Understand and optimize the customer journey. 

Tracking user experience across all digital assets of a company gives better insights into improving that experience.

Because of the focus on data, marketing departments can often find lots of options for getting marketing data out of siloed SaaS products like DSPs and into a central location for reporting and analysis. Product owners can understand how products are being used. However, combining this data with other operational data from a company can require a more custom solution.

Predictive Analytics Platform

With solid metrics established, you can identify trends and contributing factors to know how to influence your desired outcomes. Predictive analytics combine automated statistical analysis (sometimes called "machine learning") with historical data trends to create a predictive model. This model can then receive new inputs and make a prediction on the expected outcome, and how probably the outcome is. 

Predictive analytics are powerful, but they require a solid data analytics platform in place before they are feasible.

Business Analytics Platform

Bring all your business systems into a unified reporting model to get a holistic understanding of your customers and operations.

All businesses use specialized operational systems to run their business. Each department even has multiple tools.

Bringing all this data together into one place is powerful. After the first step of bringing the data all together, combining that data into cross-department metrics gives a level of visibility and decision-making many companies do not have. 

Marketing Analytics Platform

Bring data from all your marketing campaigns into one place to understand the effectiveness of all your marketing activity.

Digital marketing analytics are highly data driven, and there are many products on the market that solve this niche data analytics need. 

Yet, we regularly run into customers who need more. Their business is unique, and these out-of-the-box solutions don't fit.

With marketing analytics, the best approach is hybrid--use what is prebuilt and available, then combine it with customization to get exactly what you need.

Big Data Analytics Platform

Stream large datasets to a centralized data warehouse to identify aggregate trends and identify anomalies.

Big data is larger, faster, and more constant than typical reporting needs. Tooling exists to handle very large data loads, but it can be expensive. 

However, the same benefits apply: pulling data together into a single, central location helps understand costs, control costs, and create a centralized, unified view of important insights.

Embedded Analytics Platform

Most companies possess data that holds immense value for their customers. With embedded analytics, you can present these customer-facing analytics in a manner that's both secure and user-friendly.

Whether your aim is monetizing data, adding value to your core services, or transparently reporting your service activity, using an advanced data analytics platform will be very beneficial.

There are several methods for you to share insightful data and analytics with your customers:

1. Interactive dashboards embedded directly within your application.
2. Standalone data products for your customers, allowing them to dive deep into insights.
3. Scheduled reports and automated messages.
4. Direct data set shares for comprehensive analysis.

Embedding analytics doesn't just offer value to your customers; it establishes trust. They see the transparency in your operations and the value proposition in real-time. This builds stronger customer relationships and differentiates you from competitors.

Be sure to look for an embedded solution that’s designed with security at the forefront. Ensure that the right people see the right data at the right time, without compromising on data privacy or integrity.

Web Analytics Platform

Simplify web analytics and integrate it with your broader reporting strategy.

Web analytics is another category that is largely data driven. By leveraging tools readily available, companies can quickly get a clear understanding of their web traffic and user experience.

Combining those insights with the rest of the company's operational data results in more complete, more powerful decision making and visibility.

Self-Service Data Analytics

Make everyone in your company data-driven by giving them the tools to explore data themselves.

Self-service data analytics is the result of investing in specific tooling, encouraging and promoting data use, updated processes and training, and creating a culture of data-driven decision making. 

A data-driven culture results in more innovation and faster decision making. This starts by committing to unlocking data and exploration tools for everything in the company.

Customer Analytics Platform

Understand your customer journey across all your business systems: marketing, product usage, customer success, support, billing, and finance.

Sometimes called "Customer 360," because these efforts focus on providing a holistic picture of how a customer is experiencing and interacting with a company. Combining data from across the customer journey sheds light on bottlenecks and areas of poor performance. 

People Analytics Platform

Optimize recruiting and hiring efforts by standardizing metrics and getting them into the hands of decision-makers.

Often overlooked are people or HR analytics. Sometimes this data is sensitive enough that companies decide not to combine it with the rest of the company's data for data security and compliance in BI concerns. Or they combine non-PII data, enough to understand performance related to business operations.

Recruiting, interviewing, hiring, training, managing careers, and terminations represent an independent process in an organization, complete with a variety of vendors and products. These can result in data siloes best served by bringing data into a dedicate place for people analytics.

What is the advantage of using a fully-integrated cloud-based data analytics platform?

Historically, vendors provided fully-integrated data analytics. These proved to be monolithic, expensive, and hard to maintain. The modern data stack unbundles these capabilities into a set of tools that are cloud-based and follow modern data practices. However, the number of tools and the complexity to integrate them brings many data initiatives to a standstill.

You should look for an advanced data analytics platform that integrates these tools into a cohesive solution. The advantage of this is that you no longer have to worry about the technology or tools, and you can focus on your data analytics. 

Benefits of Using A Data Analytics Platform

According to Forrester research, companies that are data-driven grow at 30% on average, compared to 3% for all companies. That’s a 10x difference in growth! 

• Companies that provide customer-facing analytics increase top-line revenue growth, reduce churn, improve customer relationships, and differentiate themselves from their competition.
• Companies that analyze customer product usage and customer behavior increase upsells, increase customer satisfaction, and reduce churn.
• Companies that analyze key financial metrics make better strategic decisions based on empirical evidence. 

With more data available than ever, now is the time to invest in a complete data analytics platform. Below we discuss a few of the many advantages and benefits of using a data analytics platform.

Data Consolidation & Centralization

With all your data in one place, you can ask questions across all your systems. And you set the foundation for more advanced analytics platform uses.

Instead of manually downloading CSVs and spreadsheets, all your data can be brought together automatically. Data from various sources is standardized to a consistent structure, ensuring reliable analytics.

Everyone in your organization can have access to the data they need, in a consistent way. Your centralized data remains secure and protected against breaches, adhering to the highest industry standards. And data remains secure because permissions and rules can be applied all at once.

Getting data centralized and consolidated is the heart of any enterprise analytics platform. It allows you to analyze all of your data together, regardless of where it came from.

Data Transformation (KPIs & Metrics)

KPIs and metrics provide critical visibility into your customers and operations. With the right data analytics platform, not only are these metrics tailored to your unique business needs, but they are also always fresh, consistently available, and reliable.

Look for a data analytics platform that's meticulously designed to derive and present these metrics, ensuring that individuals across your organization have the insights they need, when they need them. As your business evolves, so can these metrics—reflecting the dynamic nature of modern enterprises.

The right data analytics platform will seamlessly blend data from various sources to craft holistic metrics, eliminating the traditional barriers posed by data silos. Look for a data analytics platform that has an automated data pipeline, so that you're assured that your metrics are perpetually up-to-date.  At Datateer should any issues arise, our proactive system generates a service desk ticket, ensuring rapid resolution.

Data Dashboards & Data Products (Visualization)

As humans, we naturally gravitate towards visualizations and interactive exploration—it's simply more intuitive than sifting through spreadsheets or static reports. Using a data analytics plaform ensures your insights are not just numbers on a page but a vibrant, engaging experience.

Any business intelligence or reporting product can seamlessly connect and consume data from our analytics platform. Be sure to look for a "Data Crew" that specializes in building and managing self-service data analytics tailored to your business.

Not only does this enable quicker decision-making, but it also empowers your team members–no matter their tech proficiency–to dive deep into the data, ask questions, and derive meaningful insights. Your data becomes a visual story that everyone in your organization can understand, interact with, and use to drive impactful business decisions.

How to Choose the Best Data Analytics Platform?

This is a bit of a trick question! No one tool or product is going to be a complete solution. The ones that advertise themselves as such are spread so thin that they do not do anything well. 

In our experience, the absolute best approach is to create a solution that addresses the type of data analytics you need, with the processes and tools to support it.

Assess Your Data Requirements & Objectives

It is extremely easy to get lost in a sea of vendors, product categories, and exciting new capabilities. Before beginning any sort of evaluation, be clear about why you are pursuing a new data strategy and what you hope to get out of it. 

Executive support is non-negotiable, and everyone should be aligned on the objectives and expected outcomes.

Scalability and Performance

Modern vendors are cloud-based, and the cloud data warehouse is the central piece of any analytics solution. Be evaluating the number of data sources and the amount of data you need involved, you will be prepared to evaluate any vendor's past performance on type and scale of data. 

Automation is a critical piece of a data analytics solution. Even if your "version 1" is manageable to maintain manually, this will change. As your data analytics prove to be valuable, more data sources will emerge, more questions will need to be answered, and more opportunities to take action from data-driven decision making will arise.

Ease of Use and Accessibility

An honest assessment of the data literacy in your organization will be valuable to you when choosing which tools to use and how to design processes. 

Complicating this is that data literacy and technical capability are not spread evenly throughout an organization. Different departments and different people within those departments will be expecting different levels of tools.

In general, learn whether you are working with a group of people who want tools that provide flexibility and explorability, or people who want a more curated experience, with specific questions thought through in advance and answers delivered regularly.

Cost and ROI

There is a wide variety of costs involved in data analytics. Even more importantly, there is a lot of variability in how vendors charge. Many vendors--because they are built on cloud infrastructure--have adopted a practice of variable pricing. This can wreak havoc for CFOs and budget holders. 

One method to address this is to start small and iterate, learning how charges manifest themselves as you grow into data analytics. Another approach is to seek out vendors who commit to more transparent, predictable pricing.

Vendor Support and Reputation

Invariably your organization will be learning new tools and processes. And it is almost too easy to start a new company in data. Even Datateer's Product Evaluation Matrix is not a complete list--and that's just one category of tools. One journalist does an annual survey of all the available tools. It is mind boggling.

Most companies will happily provide you with big-name logos and case studies, if they have them. Asking about case studies and testimonials from companies in your same industry or use case can be even more revealing.

Data Analytics Platform: Build or Buy?

A decade ago, companies like Talend and Informatica sought to do everything in one product. Data is way too complex for that today. The specialized tools of today are orders of magnitude better than monolithic vendors.

The big question is how much to do in-house vs whether to find a partner that can bring vetted tools, best practice processes, and specialized skills on demand. 

Some companies believe no outside party should be involved with their data. I respect that opinion but disagree with it. Data is proprietary and valuable--but data operations are not at all. That's like saying I need to custom-build my car. No one would do that today except for enthusiasts. Instead, I want to buy a car that is reliable, fast, and that checks my other boxes--then let me take that car wherever I want to go.

In fact, most companies in the zero-interest rate investment environment of the early 2020's burned money like crazy, overhiring and overspending on tools. They each designed custom processes, figured out how to integrate tools together, and lost momentum by getting caught up in the tech.

Datateer's Managed Analytics is a service that bundles excellent vendor tools, has a pre-built data architecture, and is constantly iterating and improving our monitoring and processes.

In my experience, companies or departments of fewer than 1,000 employees should strongly consider outsourcing some or all of their data operations. Those who do can focus on their data and business insights, instead of the technology, infrastructure, and data operations.

Conclusion

Data analytics is going through an extremely interesting time. Every company benefits from it and understand the benefits--but most companies lack the capability to have excellent data analytics. 

By understanding the fundamentals of a data analytics platform solution, you can be more informed to decide how to execute on your data strategy, and who to involve to get you there faster.