• TDWI Strategies for Improving Big Data Quality

    Organizations dependent on big data for a wide range of business decisions need data quality management that can improve the data so it is fit for each desired purpose. Without data quality management, the massive quantities of data organizations ingest will not provide the anticipated benefits—and can even do harm if used to drive faulty business decisions.

    This TDWI Checklist Report offers six strategies for improving big data quality. In discussing these strategies, we will look at how managing data quality in big data environments differs from traditional systems.

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  • Hadoop ETL checklist

    Want to replace traditional ETL approaches with Hadoop to deliver more quickly and more cost effectively.

    This free ebook lets you know what you need to think about to get this right

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  • Five data engineering requirements for enabling machine learning

    Although the technology behind machine learning has been around since the 1990s, the advent of big data has both revitalized it and increased the complexity of using these models to drive insight and action.

    One of the biggest challenges facing companies that want to take advantage of machine learning is making the leap from the training phase to full production. Data engineers must create robust production data pipelines to feed machine learning models the increasing amounts of disparate data they require.

    This TDWI Checklist Report discusses best practices for data engineering and management to support machine learning with a focus on collecting, cleansing, transforming, and governing new types of data for analysis.

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Big Data Solutions

big data iconBig 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 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 to 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 business analysts and business users that need it, while providing the necessary governance to allow IT to keep control




Why Big Data?

Big data has received a lot of attention - some may say hype - and has seen little practical uptake in Southern Africa.

However, in Europe and the United States businesses that have adopted Big Data have achieved significant head starts over slower moving competitors.

In areas such as data driven marketing, fraud analytics, customer experience and channel optimisatoin, and even simply to reduce teh cost of running and maintaining the traditional data warehouse, big data has proven value.

What is Big Data?

Big data is confusing, particularly as every vendor and every consultant attempts to position their solution as a big data platform

Big data is frequently described in terms of the three V's - Volume (the size of the data), Variety(the complexity of multiple data sources) and Velocity (rapidly changing data).

In fact, big data must combine at least two of these attributes, making it both more complex and potentially more Valuable that traditional data sets.

The real value of big data comes from the fact that it does not require complex, time consuming data base design - like a tradtional data warehouse. The schemaless nature of big data cuts design and integration by many months allowing you to answer your complex questions in busniess time. 

How is big data implemented?

Big data is typically implemented using Hadoop, an open source software framework for storing and processing big data.

Hadoop is able to store pretty much any data type (Variety), uses commodity hardware to handle large Volumes cost effectively, and scales cheaply, through distributed processing, to handle Velocity. Our solution takes advantage of these inherent strengths of Hadoop - low cost and high scalability - but adds ease of implementation and a range of management features.

The Fourth V - Value

Our focus is on unleashing this Value, the fourth V, through a combination of self service tools and a simple methodology that reduce big data's complexity and allow you to get to insight quickly

Our Big Data methodology 
prepare integrate analyse visualise
eBook: Buyers Guide for Big Data analytics eBook: Big Data Buyers Guide    
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  • 4 benefits of a business glossary

    Data Quality Matters Jan 15, 2019 | 08:05 am

    In its most basic form a business glossary is a set of definitions for commonly used business terms, aggregations, etc Many organisations may start with a spreadsheet of terms. However, over time, the need to provide context to the business[…]