How do I select a data analytics solution for my business?

In today’s data driven world, an organization should have full insight into its database, not only because it is important to have control over it but also because it can drive business and strategy decisions based on trends and patterns. Data analytics is and in-depth way of knowing your data and making the most of it, while protecting your assets.

To select a suitable data analytics solution for your business, you need to think about a variety of factors. We’ve talked to several industry professionals to get their insight on the topic.

Joe Hellerstein, CSO, Trifacta

Successful data analytics incorporates everyone at the organization, including non-technical users across departments. Getting data and analytics right requires a comprehensive approach that connects data experts and domain experts, hand-coders and no-coders, to blend engineering discipline with business agility.

The first thing to look for in a data analytics product is that it empowers everyone. Modern cloud products, with zero setup and easy to use interfaces, often do this well. There are no “walls” in the cloud, which eliminates any artificial barriers between teams. And cloud solutions give organizations the flexibility to scale up and scale down depending on their needs.

Another crucial thing to seek is the ability to see the data. When users can visualize the data at every step – as they refine, transform and analyze it – the quality and speed of the results increase dramatically.

The most advanced products combine visual interaction with AI/ML intelligence to recommend quality rules and suggest transformations that guide users to the best outcomes. In these products, AI meets human intelligence in a “guide and decide” approach so every user can maximize the value from data, regardless of how technical they are.

Finally, look for products that empower extended teams by supporting code and no-code approaches, with robust sharing and collaboration capabilities to leverage and extend existing work.

Alan Jacobson, Chief Data and Analytics Officer, Alteryx

Digital transformation is enabled with world-class data and analytic software that can accelerate your knowledge workers’ journeys to become data-driven. The three top buying considerations should always include:

Usability: Can all of my knowledge workers actually use the technology? In the end, data and analytic solutions perform math. They will get the same answers to your questions; however, most solutions can’t be used by your accountant, your HR professional, or your marketing operations expert and instead can only be leveraged by data scientists. This simply won’t get the best outcome, as you do not have enough data scientists to answer all your business questions.

Breadth: Does the solution span the full continuum your business needs? Can it help clean and manipulate data from any source? Can you perform geospatial analysis? Can you easily automate a process? An end-to-end platform that solves for all of your data and analytic needs can replace any disconnected, clunky point tools that only perform specific functions.

Outcomes: Are you able to quickly benchmark and see the outcomes the technology is delivering today? Are you able to trial the product for 30 days and get outcomes yourself? With an outcome-first approach, you can spend more time focusing on business impact and less time upfront completing tedious back-end work.

Vishal Kasera, Senior Director of Product, ThoughtSpot

When selecting a data analytics solution for your business, it’s important to look at the use case for which you’re trying to solve. Are you trying to grant C-suite individuals access to macro-level analytics, or are you interested in putting actionable insights in the hands of the everyday business person for them to directly react to?

I’d argue enterprises benefit most when all users are empowered to access, interact and derive insights from data themselves. This leads to increased productivity and efficiency, and inspires rapid, data-driven, decision-making which drives organizations’ bottom lines.

Business and data leaders need to prioritize a solution that offers a familiar consumer-grade experience which is simple and intuitive for all users. Asking them to learn a tool that requires multiple days of training means that the analytics solution is dead in the water. When it’s as simple as search, however, people instinctively start unlocking value.

It’s even more powerful when users can then use those insights to automatically trigger action in other apps or services. All of this has to happen with large volumes of data without sacrificing the controls, security, and governance requirements associated with managing the variety and volume of data today.

Monzy Merza, VP Security GTM, Databricks

Data driven companies are 19 times more likely to achieve above average profitability. Data analytics solutions drive decisions that create competitive advantages. Choosing the right analytics platform isn’t just an IT decision, it’s a strategic imperative that will impact the daily work across the organization. Making a sub-optimal choice will be reflected on your balance sheet.

The right solution must fit your business culture, be aligned with your IT strategy, be built on modern tech and be economically advantageous. Culturally, the solution must provide fast time to value to business users, operational analysts and serve engineers, developers and data scientists. Strategically, it must be an open system to integrate with your IT and security operations and have compliant governance controls. Technologically, it must be multi-cloud native to give you the scalability of workloads and freedom of vendor choice. And economically, its pricing must be attractive, transparent and predictable to grow with your business.

Recognizing these requirements, leading global brands in every industry vertical are employing cloud native lakehouse architectures. The architecture combines the advantages of data warehouses with data lakes via managed cloud services. Not all lakehouses are open platforms or multi-cloud capable. Ask those questions if you want to avoid vendor lock in or if you expect to operate in multiple geographies or if you have independent subsidiaries.

Defining the business problem is of utmost importance. Based on the business problem, you need to identify what sort of data is required for your task. This is very crucial as it may lead to Garbage in – Garbage out.

Now that you have identified what kind of data is to be collected, the next step is to pinpoint the source of this data. Internal sources, external sources, paid data sources etc. can be leveraged, tool stacks that can be used, starting from data collection to insights building.

Consider the following while selecting your data analytics solution:

• The data pipeline that you are intending to integrate should be compatible with your other applications. Create an ecosystem.
• Your data pipeline should be agile, scalable and future-ready.
• You should get real-time reports rather than post facto insights.
• Perform a cost-benefit analysis before selecting and deploying any analytics solution.
• It should not compromise with your data security.
• The learning curve for the people using the solution should not be steep.
• Start small, start now.

A lot of times, businesses tend to get deviated with buzz words like ML/AI, deep learning, big data etc. We need to understand that an organization can not be ML/AI compliant overnight. It takes time. It is key to start with basic and small analytics solution, learn from it and keep building on it.

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