We work with many technology providers in each of the above-mentioned disciplines. We are not aligned to any particular technology, but we do partner with vendors who we consider to be leaders in their respective disciplines. We continue to activity work with database vendors including Oracle, Teradata, Microsoft, IBM and some of the niche data warehouse in memory database providers.
For Database and Data Privacy, Auditing, Compliance & Security, we exclusively work with and fully support the DataSunrise Data and Database Security Platform found at https://www.datasunrise.com/.
In addition, we engage with other vendors who request us to achieve similar sales, consulting and certification status with their respective products.
We offer a varied range of Advisory and Consulting Services to assist and drive your Data Innovation journey. Learn more here.
These traditional, now fast becoming legacy, environments have been around for the better part of 20 years, and created the foundation for early reporting, analytics and data quality initiatives. They are still prevalent today, although, the onset of far superior technologies and a dramatic shift in business urgency, have diminished constant upgrading in favour of a compete technology refresh to faster, less expensive and more notably, cloud-based environments.
Existing systems will morph into a central repository for enterprise data stores (e.g. Data Lakes) as a continued use of them, whilst business critical analytics and decisioning will move to nimbler and business user focused environments. Enterprise cloud providers like Amazon Web Services, Microsoft Azure and Google Cloud all offer environments to house data warehouse environments, and many migrations are using, and will continue to use them.
Let’s remove the hype and focus on the fact that Big Data is simply another industry marketing term used to describe the next generation of traditional data warehouses which organisations use to improve operations and increase profitability. The new intent is to create valuable insights from processing data, and with a focus on personalisation and customer centricity, it’s ultimate goal is to regain lost competitive advantage caused by new technology entrants in the market place and the growing sophistication of the business user.
With the vast amount of data accumulated in operational systems over decades of business operations, business may make faster and more informed business decisions, provided they use the data effectively. By example, they may discover valuable insights about customer behaviours which may be used for targeted marketing campaigns and cross sell opportunities to drive and increase customer engagement. As well as the traditional sources of data from operational systems, new and varied data sources emerged including, internet clickstream logs, mobile applications, social networks, scientific research repositories, machine-generated data and real-time data sensors used in internet of things (IoT) environments. Many organisations store this data in Hadoop environments.
Streaming data is a natural extension to Big Data. There are different names for streaming including Data Replication Streaming, Real-Time Analytics, Streaming Analytics and even Complex Event Processing. However, in essence, streaming allows continually generated full or incremental data sets, from a variety of different data sources, to be continuously queried (processed) in real time, allowing immediate actions or business triggers to occur based on the data been processed.
Generally, these can be instantaneous, a few milli seconds or whatever time period is demanded by the business. These real time insights enable organisations to be more responsive to customer behaviours, market conditions and competitive forces. Streaming, or Big Data streaming is in reality a speed-oriented approach where the continuous stream(s) of data are processed. While Big Data establishes the value of insights as understood at the post processing of the data, the value insights created by streaming data are typically different – that is, real-time, and do not necessarily have the same value. For example, some insights are of far more value shortly after an event has happened (like a currency trade) with the value diminishing very fast over time. Storm, Apache Spark, Apache Flink, Apache Kafka, Amazon Kinesis are all examples of popular streaming technologies, with HVR the most popular focused Data Replication solution for real time data synchronisation and streaming.
As above, we have greater than 30 years’ experience in all of these environments across past and present technology providers including Oracle, Teradata, Netezza, SQLServer, Greenplum, Vertica, DataSunrise, HVR, Hadoop and Apache Spark to name a few. Please Contact us for a discussion on assisting with your current environment, migrating to a new one or undertaking a refresh.
In a nutshell – GiGo. That is, Garbage In, Garbage Out.
Resolving data quality problems is often the biggest effort in an organisation. Data Quality has Operational, Tactical and Strategic uses, and poor data quality will dramatically affect a company’s bottom line. I would imagine the executive management of a company would be embarrassed when they learned that they informed one of their customers that our company database shows they were deceased. It has happened more than once. The social media impact from that alone would easily send their stock price down.
While data is generally considered high quality if it is "fit for its intended uses in operations, decision making and planning activities”, this is not always the case. Data quality is a function of its use, not its collection. A database that is perfect today will include errors tomorrow. Data Quality should be an integral part of overall organisational data governance and in unison with your Data Quality Management practice – all aimed at maintaining high quality of information. You need to develop clear and operable definitions and nomenclature of data requirements, which are properly interpreted and accepted by the business, and, provide assurance that data is used for only legitimate business purposes and most importantly, protecting the rights to privacy of your customer’s data. Data quality characteristics should be monitored constantly and any variances attended to immediately. These data quality characteristics may be broadly defined accuracy, completeness, reliability, relevance and timeliness.
What is your organisational level of confidence your data quality? This is the oil of the future.
Data Privacy and/or Information Privacy is the component of data security concerned with the ethically correct means of handling of data – consent, advice, notice and regulatory obligations. Practical data privacy concerns often revolve around whether and/or how data is shared with multiple organisational divisions and third parties and how data is legally collected or stored and where it is stored.
Whilst Data Warehousing, Big Data and Streaming Data has already had a large impact on innovation and for businesses, it is more than likely that this impact will continue to grow in the future. Business need to be aware of rapid industry, legislation and governance changes in order to keep up with and innovate. More data sources will be found, data sources will be created and organisations will create newer means of searching data. As this data is turned into Information then Knowledge and then Power, organisations face internal and external challenges in protecting their data from cyber threats and theft.
An equal concern is Data Privacy, which will continue to be a vital and important issue in business as the volume of information increases, and, the way of analysing it. Organisations will need to understand that not all captured data will be allowed to be used in analytic streams, and they will need real time governance in place to ensure compliance. Regulatory Compliance with SOX, HIPAA, GDPR, PCI DSS, Opt-in, Opt-out and other privacy laws and standards is a business and legal imperative.
Organisations need multi-level, multi technology data and database protection in place to protect their information assets and deter these threats.
As above, we have greater than 30 years’ experience in all of these environments across past and present technology providers including Oracle, Teradata, Netezza, SQLServer, Greenplum, Vertica, DataSunrise, HVR, Hadoop and Apache Spark to name a few. Please Contact us for a discussion on assisting with your current environment, migrating to a new one or undertaking a refresh initiative.
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