Big data brings together more data, from more sources in order to to provide companies with better insights into their business.

Companies that make effective use of Big Data and Analytic increase their productivity and profitability by 5 to 6 percent over those that don't.

Yet, for most companies, big data analytics remains a bridge too far.

Obstacles such as the technical complexity of new "big data" architectures, combined with the high cost and scarcity of skilled staff, stop many big data initiatives in their tracks

Our approach combines education. consulting and market leading technology to simplify and democratise big data - placing decision making in the hands of the data scientists, business analysts and business users that need it, while providing the necessary governance to keep control

Big data and advanced analytics allow businesses to make informed decisions for business improvement

Big data brings together more data, from more sources in order to to provide companies with better insights into their business. 

Core to big data is the principle of data discovery - identifying previously unknown patterns of behaviour about our customers, our markets, our offerings and our operations that allow us to radically improve the way we do business.

Big data is not just another BI tool - it is a whole new way to deliver insight

Companies that are able to make effective use of big data and analytics increase their productivity and profitability by 5 to 6 per cent over those that don't.

Catalogue

Catalogue

Find and share analytics data sets

 

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Enterprise Analytics Hub

Companies are looking to gain better insights, more quickly, by drawing in more and more data from a variety of sources - including traditional databases, legacy systems (like mainframes), and, increasingly from unstructured data sources such as the Internet of Things and Blockchain.

This complexity and volume can become overwhelming

Many data scientists spend more time trying to find and access data than delivering insights. In recent years, we have seen the emerging new role of the data engineer - solely responsible for the delivery of quality data for the use of the data scientist and other knowledge workers

It is important that these data engineers document their work - providing curated and documented data sets in a data catalogue that can be easily searched and supports data-sharing agreements.

Companies must also consider the use of self-service data preparation tools to allow more users to build robust data pipelines.

Big data training

Big data training

Data Science and Analytics courses

 

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Big Data Preparation

ETL (extract, transform and load) has for years been a workhorse technology for enabling analysis of business information. But now it’s being joined by a new approach, called data preparation or data wrangling. The two techniques are similar in purpose, but distinct in function and application. 

Where ETL is intended for IT professionals, and works mainly with structured data set, data preparation engines for big data must handle data of any type - both structured and unstructured - and is becoming more an more a self-service capability enabling both business and IT professionals to find and access the data they need to deliver trusted insights.

Precisely Connect seamlessly connects legacy data into next-gen cloud and data platforms with high-performance and low maintenance ETL and CDC capabilities.

Infogix Data360 Analyze is our drag-and-drop data preparation and analytics tool that simplifies the process of building data pipelines to rapidly achieve business insights.

Data360 Analyze

Data360 Analyze

Empower users to rapidly gather, combine and organise data for analysis

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Big data quality

Your first thought might be analytics accuracy or the amount of data you have available to process. But if you’re not thinking about big data quality as well, you may be undercutting the effectiveness of your entire big data operation.

Why?

Consider the following ways in which data quality can make or break the accuracy, speed and depth of your big data operations:

  • Real-time data analytics are no good if they are based on flawed data. No amount of speed can make up for inaccuracies or inconsistencies.
  • Even if your data analytics results are accurate, data quality issues can undercut analytics speed in other ways. For example, formatting problems can make data more time-consuming to process.
  • Redundant or missing information within datasets can lead to false results. For example, redundant information means that certain data points appear to be more prominent within a data set than they actually should be, which results in misinterpretation of data.
  • Inconsistent data – meaning data whose format varies, or that is complete in some cases but not in others – makes data sets difficult to interpret in a deep way. You might be able to gather basic insights based on inconsistent data, but deep, meaningful information requires complete datasets.

We help you to leverage big data technologies to cleanse and match data at extreme scale

Trillium DQ for Big Data

Trillium DQ for Big Data

Maximize the business value of big data and cloud

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Dynamic Access Management

With universal data authorization from Okera, data scientists can get on with the job you hired them to do, which is harnessing your data to drive the business forward.
 
Stop wasting valuable time engineering and provisioning one-off data sets for narrow use cases.
 
Give data scientists the ability to use their preferred tools, and dynamically and securely grant access to data where it lives.

The 4 Pillars of Data Transparency

Making it easy to find, understand and access the data you need

Case Study

Case Study

Reducing AML risk and complexity with big data technologies

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TDWI Checklist Report

TDWI Checklist Report

Five Data Engineering Requirements for Enabling Machine Learning

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Debugging data

Debugging data

Why Data Quality is essential for AI and Machine Learning

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Read our blog

Read our blog

Why Data Quality is essential for AI and Machine Learning

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