Data Analysis

Enter your search above to see Results

Modern Techniques to Process Your Data

Having the best Data Platform possible only gets you part way to good data science. The next step is being able to process the data effectively and run the algorithms that you need. For this you no only need the right tool but you also need the right team. At JTA our teams are selected from three distinct disciplines for the best possible service.

Data Analysis Must be Scalable

We have all seen that data quantities have grown over recent years. This looks set to continue. In addition, our pool of historical data will keep increasing. A data analysis platform must be able to grow with the business. It should also be flexible. A business may wish to undertake an exercise that needs to process more data than usual, but only for a short term.

Data Analysis Must be Cost Effective

It really goes without saying that data analysis platforms should not be too expensive to run. Unfortunately many organizations have trouble keeping costs under control. This is not simply an engineering issue. The algorithms we choose, the format of storing our data and the way we plan our work all have a great impact on cost.

Data Analysis Must Integrate with the Business

Our data analysis platform should deliver great, actionable insight. But what happens after? A strong data analysis platform will be able to integrate and support existing business processes so that insight becomes part of the business culture.

Eight Attributes of a Strong Data Analysis Platform

One: It Must Wrangle Data

As any good data scientist knows, data never quite comes in the format you want. For example, a great deal of the day-to-day work of the data scientists involves the manipulation of data. Pivoting, summarizing, filtering the work never stops.

Two: It Must Support Data Exploration

Before knowing what to do the data scientist typically spends a lot of time just exploring the data that they have. Thus, a strong platform must have tools and mechanisms to see the data from both a high level summary and at the very detailed level too.

Three: It Must Support Good Data Governance

Data governance enables an organization to ensure that high data quality exists throughout the lifecycle of the data. The key areas of data governance include availability, usability, consistency, data integrity and data security. Very often data governance is a set of processes but the data analysis platform must be able to implement those process effectively.

Four: It Must Support Version Control

Five: It Must be Scalable

Six: It Must be Cost Effective

Seven: It Must Give Access to Existing Algorithms

Eight: It Must Embed With Legacy Processes

The JTA Data Science Team

Projects are always tackled by a multi-skilled team under an experienced project leader, usually a partner in JTA. Projects start with a lot of listening. Understanding our client’s concerns and plans is paramount to us. We simply will not start on a project unless we can clearly see the business need, the path to unlock the solution and the value that we will bring.

Different clients have different requirements when it comes to their involvement in the project. For some clients we adopt a close partnership, often employing a project manager who may liaise with our clients’ project management. In other cases, we provide a turnkey solution asking for very little input from the client during the project. The choice is entirely up to our customer. In house we use Agile development methodologies aided by sophisticated project management and monitoring tools. A GIT repository stores the source code, examples and documentation associated with the project for version and release control.

The Secret Recipe to Success

This about the modern systems which are now available. Don't just return to using the database for everything. Databases offer behavior known as ACID. ACID (Atomicity, Consistency, Isolation, Durability) is great for processing transaction but can get in the way of fluid and rapid experimentation. You don't want to wait for a database to log your every move just so it can recover.

Why not consider the cloud? There is still a desire to keep data analysis platforms in house. Managing systems is a lot easier in the cloud. All of the work to scale, ensure uptime, do backups and so forth is done for you.

Think about the format in which data is stored. The value "123" when stored as a string will occupy far more space that the integer 123. Even better might be a one byte integer 123. To really do this well we need to consider all the possible values that the data might take. Saving just a few bytes on each row of data will make a huge difference when we look at the full data set of millions of rows.

Data Scientists still think about how a human would like to see data and not how a machine would like to see data. Input data should not be arranged in neat columns. Input data should not be formatted.

Frequently Asked Questions

How Do I Become a Data Scientist?

It is necessary to undertake a journey to become a data scientist. One of the best ways to become a data scientist is to join a reputable data science provider like JTA. Data Scientists need to solve problems in a logical and analytical way. Mathematical ability is important, and you will need to understand some algebra, statistics and probability. If you can handle calculus, then so much the better.

The main languages used for Data Science are Python and ‘R’. You will need to learn at least one of these languages.

Next, you will need to understand how data are stored and manipulated, however pay careful attention to big data concepts and techniques.

If you would like to know some more then read about How JTA The Data Scientists does its work or have a look at some other FAQs.

You could also explore our case studies or whitepapers.

What is Data Science?

In 2012 the Harvard Business Review called it “The sexiest job of the 21st century”. Some claim that it is nothing more than a sexed-up term for statistics and so a lot of confusion reigns. We believe that Data Science is a merger of many traditional disciplines, bringing together statistics, processes, algorithms and machine learning. This means that it can have different interpretations but at its heart data science is the extraction of knowledge from data.

https://en.wikipedia.org/wiki/Data_science

If you would like to know some more then read about How JTA The Data Scientists does its work or have a look at some other FAQs.

You could also explore our case studies or whitepapers.

What is the future of Data Science?

In the future of data science we will discover Causality without needing to understand the “why” or “how”. As data volumes increase, we discover patterns that may trigger us to investigate why. Data Science finds patterns so that humans solve problems that we didn’t know we had. This has an immense impact on our lifestyles. We will start to truly understand the impact on our lives, diets and behaviour.

If you would like to know some more about the future of data science then read about How JTA The Data Scientists does its work or have a look at some other FAQs.

You could also explore our case studies or whitepapers.

Related Case Studies

The Adoption of Telemetry

How we help a market research team to start analysing telemetry data and to get over the large cultural change needed to adopt big data.

Adding Value to a Market Research Survey

This case study describes how we used data science to mine the data from a worldwide market survey in real time. The system warned about low confidence limits and low sample sizes so users would not focus on results that could not be justified.

Targeted Selling at a Software Company

How we used a combination of internal an external data to drive sales using OLAP and PowerBI.

Our Latest Testimonials

The online solutions provided by JTA are very powerful and dynamic. Working with JTA is always a pleasure and a trouble-free experience. Consequently, our relationship with JTA is very collaborative. JTA always handles any challenges promptly and without stress.

Raymond Piombino, Founder, Bordeaux Consultants International

JTA can be trusted to get the job done right, on budget, and on time. I literally NEVER worry about errors when working with them – they are very thorough, has an excellent eye for details, and are pleasant to work with. I look forward to working with them again in the future.

Jim McDonell, Analyst

Which industries use our innovative approach to Data Analysis?

We work across several sectors providing data solutions that inform and enhance your business. Whether it’s confidential financial information, life-saving medical records or software to improve the gaming experience of your users, our dedicated team are agile enough to deal with all needs.

Software