9 essential tools for data analysis, visualisation and insight that you need to look at today

by | Mar 8, 2017 | BlogPosts | 0 comments

The power of Business Intelligence (BI) is no longer reserved for industry giants. Data-driven insights aid profitable decision-making, no matter which sector you operate in. Choosing the right tools for analysis and visualisation ensures a definitive edge in a competitive marketplace.

Both novice and experienced analysts are benefitting from more intuitive software. But not all tools are created equal. Companies handling large quantities of data need reliable programmes that offer significant predictive and modelling power.

Here are nine data-analysing tools that every forward-thinking business needs to consider:

BI for beginners

If you’re looking for a platform your entire workforce can implement, these tools will get the job done:

1. Caspio

Caspio makes data analysis accessible to everyone. Built on Microsoft’s SQL server, this web-based platform requires zero coding experience. The drag-and-drop features allow you to convert spreadsheets and databases into interactive reports in an instant.

Caspio’s user-friendly interface makes it ideal for non-developers. Applications and visualisations can be sculpted from scratch using information from your own cloud-based data warehouse.

2. Vizydrop

Looking for quick and easy visualisations? Vizydrop uses data from CSV, Excel and JSON files to create customisable graphs and charts. There’s no prerequisite to learn code and the parameters for each data set remain in your hands.

Vizydrop’s free web app simply analyses the data and determines the most efficient from of visualisation. Not happy with its choice? Click on various suggestions to view your data from another perspective. All Vizydrop’s selections are based on machine learning algorithms, understanding your requirements a little better with each new entry.

3. Tableau

Tableau sets a new precedent for agile data analysis. Scalable, intuitive and user-friendly, this tool enables anyone to interpret patterns and trends across a variety of databases.

Tableau’s real-time dashboard constructs live visuals that adapt to changing data streams. Relational and non-relational data can be combined from spreadsheets, SQL servers and cloud applications. The platform’s ‘no-code’ approach lets you access and query disparate data sources to achieve fresh insight into ostensibly unconnected areas of your business.

Applications for amateur analysts

For aspiring data scientists and those looking to learn a new skill:

4. Splunk

Splunk gets to the heart of machine data. Its algorithms take into account customer behaviour, machine behaviour, sensor readings and cybersecurity threats. The software is available as both a server and cloud-based packaged, with Splunk Light geared towards smaller IT environments.

Machine data is particularly useful in industries where risk management is integral. Splunk enables the real-time deployment of data visualisation to global servers and devices. Working with systems such as Hadoop, Splunk provides Operational Intelligence for business and security performance.

5. ZoomData

Profitable data analysis always solves a problem. For example, in the insurance industry, data from both in-vehicle sensors and online credit checks are used to improve the quality of a firm’s underwriting. ZoomData is a dynamic data analysis platform, designed with real-time visualisation in mind.

Low latency tools enable business leaders to make smarter, more relevant decisions. ZoomData’s interactive visualisations produce actionable big data insights. Combine big data, legacy data and streaming data in one platform, with a package built to integrate with Hadoop and Spark.

6. PowerPivot for Excel

Excel remains a hallmark of data analysis. Run on the Microsoft SQL Server, Power Pivot increases the power and agility of Excel’s data modelling processes. Those familiar with Excel will recognise existing data functions and find the new add-in easy to navigate.

The latest Power Pivot instalment is native to Excel 2016, working alongside Microsoft Power View to analyse and visualise millions of data entries. Databases on the SQL server can be blended to display trends in relational data.

Big guns for big data

7. Trillium

What do you get if you combine self-service data preparation with high-end, industrial analysis? The answer: Trillium. Trillium know that better data equals better business decisions.

Discover unique insight into your business processes with Trillium Discovery – a data interrogation tool that ensures the accurate analysis and regulatory compliance of all your data. Trillium’s detailed reporting software summarises its findings on a central dashboard, consolidating all data defects.

The application compares your database against industry standard values to isolate infringements. This level of data preparation ensures there are no delays in critical decision-making processes.

8. Microsoft Power BI

Power BI is the big brother of PowerPivot. It works using the same DAX (Data Analysis Expressions) language that underpins Excel. DAX allows Power BI to express relationships between datasets that would otherwise remain incompatible.

DAX isn’t a language that can be learnt overnight. But once you master it, Power BI offers an intimate insight into complex data relationships. Customised metrics improve the quality and speed of data analysis. Visualise profitability, combine both cloud and on-premises data and gather actionable Business Intelligence.

9. Microsoft R Server

R is the hottest language in coding circles. Microsoft’s adoption of R just goes to prove its analytical power. The company’s R Server works within your data warehouse to perform high-speed queries on limitless volumes of data. Deploy Microsoft R Server on any platform – on-premises, in the cloud or in a hybrid environment – to gain deeper insights into social media activity, sensor feedback and website entries.

Creating queries and visuals in the server takes considerable experience in R. But the results are significant. Microsoft R Server simplifies the processes used in Hadoop and Spark to build stronger, cleaner predictive and forecasting models.

Choose your weapon

The data analysis tools in this list perform many of the same duties. Which one you pick depends on the requirements and capabilities of your firm. Evaluate what you want from BI and choose the option that best supports these goals.

If you’re an insurance company, look for platforms that stabilise risk and augment customer relations (see Tableau and Trillium). If you’re in banking, the same applies, except you’ll also need a tool that can accurately forecast trends in the economy (see Power BI and Microsoft R Server).

Work out exactly which areas of your business data analysis can improve and find the tool to match. Still stumped? Sign up for demos from each of your favourite candidates or ask the advice from us here at Datalytyx. For more information email: info@datalytyx.com.

http://www.mckinsey.com/industries/financial-services/our-insights/unleashing-the-value-of-advanced-analytics-in-insurance

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