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Data Analytics and Visualization

DATA ANALYTICS & VISUALIZATION

65% of businesses around the world run data analytics tools to improve their business strategies

If your business doesn’t rely on data to solve problems, build revenue or track capital, then you will lose a lot.

What is Data Analytics & Visualization?

Data analytics is the science of analyzing raw data in order to make conclusions about that information.

Many of the techniques and processes of data analytics have been automated into mechanical processes and algorithms that work over raw data for human consumption.

Data analytics techniques can reveal trends and metrics that would otherwise be lost in the mass of information.
This information can then be used to optimize processes to increase the overall efficiency of a business or system.

 

Why do I need Data Analytics & Visualization?

Data analytics is important because it helps businesses optimize their performances. Implementing it into the business model means companies can help reduce costs by identifying more efficient ways of doing business and by storing large amounts of data.

Your company can also use data analytics to make better business decisions and help analyze customer trends and satisfaction, which can lead to new—and better—products and services. 

Close to 82% of businesses rely on data visualization graphics to portray business related metrics

Whether quarterly or yearly sales reports, employee appraisal analysis, leave tracker or product roadmap improvement. Data analytics and it’s visualization tools can make a huge difference.

“7x – the number of times our data analytics tool helped scale new businesses faster”

(FAQs) on Data Analytics & Visualization

Data Analytics and Visualization is the science of tracking, pinpointing, scrutinizing and implementing data-based strategies. These frequently asked questions are made up of queries that we get asked often.

You might have heard of Data Analytics & Visualization. But do you know what it means?
Data analytics enables making conclusions while analyzing data from informational resources. Data analytics uses strategies via technological processes and algorithms that manipulate data for human utilization and understanding. Data analytics helps businesses optimize their capacities.
You understand what Data Analytics & Visualization is, but can it help you?

Businesses are using analytics to make more informed decisions and to plan ahead. It helps businesses to uncover opportunities which are visible only through an analytical lens. Analytics helps companies to decipher trends, patterns and relationships within data to explain, predict and react to a market phenomenon. It helps answer the following questions:

 

What is happening and what will happen?

Why is it happening?

What is the best strategy to address it?

Collecting large amounts of data about multiple business functions from internal and external sources is simple and easy using today’s advanced technologies.

The real challenge begins, when companies struggle to infer useful insights from this data to plan for the future. Using analytics businesses can improve their processes, increase profitability, reduce operating expenses and sustain the competitive edge for the longer run.

What is the difference between Data Analytics and Data Analysis?

The difference between analytics and analysis is scalability.

Data analytics is a generalized term and is the umbrella over data analysis. Data analysis is the examination of data.
Data analysis includes data collection, organization, storage, and strategies and tools used for analysis.

Is it a good idea to hire an agency to perform Data Analytics?

Yes, it is. Building analytics function requires long term commitment and extensive resources. An organization has an option to seek analytical help from in-house resources or from outside analytical vendors or use both in parallel.

Any organization needs to spend considerable time and money to recruit and train in-house analytical help. At times they may not possess the required know-how to recruit such specialized staff or decide on the technologies that would be best suitable for carrying out analysis.

Agencies work with the management team to help the organization to adopt analytics. The organization has to trust and co-operate with the agency while sharing their data and researching it to make the analytics engagement a success.
How much time and resources are required?
The resources and time required for a data analytics project is dependent on a number of factors. The major factors being the scope and scale of the project, readiness and availability of required data, understanding of the analysis tools, skills and knowledge of the analytical team and most importantly, acceptance and approval from the management team to carry on the analytics project.

An agency such as Kilowott, defines a project timeline dependent on the factors listed above. Intermediary findings and analysis difficulties might alter the goals and objectives of the project. This might require the team to re-work the time and resources required for completing the project.
How much money does Data Analytics outsourcing cost?

For data analytical needs, an organization can decide to use data analysis softwares like SAS and SPSS, or seek help from custom consulting companies such as Kilowott to build data analytic capabilities. Today companies are even using a combination of the above.

The cost for outsourcing will depend on numerous factors that are best answered by contacting Kilowott.
 

Data Analytics & Visualization

Know Your Data Analytics & Visualization - A Quick Guide

Data Analytics and Visualization is a business science that analyses raw data to finalize conclusions.

Many of the techniques and processes of data analytics have been automated into mechanical processes and algorithms that work over raw data for human consumption.

Prescriptive analytics

This technique suggests a course of action. If the likelihood of a hot summer is measured then the subsequent course of action needs to be adopted to sell more air conditioners.

 

Predictive analytics

This technique seeks answers to what is likely going to happen in the near term. What happened to sales the last time we had a shortage of personnel? How did festivals last year impact sales?

Diagnostic analytics

This technique focuses more on why something happened. This involves more diverse data inputs and a bit of hypothesizing. Did the weather affect logistics? Did that latest marketing campaign impact branding?

 

Descriptive analytics

This technique describes what has happened over a given period of time. Have the number of website traffic gone up? Are sales figures stronger this month than last?

The ngX Framework

Based on our deep design and technical experience across industries we’ve developed a proprietary digital framework, the ngX framework, which is leveraged across all our projects.

The ngX framework consists of the following steps:

Assess

Assess your current customer experience and digital posture across all digital properties

Define

Define a digital experience transformation roadmap to capture digital moments

Develop

Develop the front-end and back-end systems and technologies to bring the digital next strategy to life

Deploy

Deploy all the technologies and design elements together after thorough QA and user testing

Support

Support the deployed components to ensure digital experience ecosystem meets its stated business objectives

Market

Digital marketing to promote the reimagined brand, acquire new customers and retain existing customers for continued business impact

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