Technology has to follow your data strategy

Technology has to follow your data strategy

Knowledge is power

Knowledge is power. With IDC estimating that the data mountain has now reached five zettabytes, it is not a case of a business not having enough data to make business decisions, but arguably knowing too much. For many, it is the old adage of not being able to see the wood from the trees. Armed with all this information, businesses should be able to operate more efficiently and accurately than ever before, but many simply don’t have the key to unlock valuable insights from the data.

 

Data science technology is evolving to allow companies to create insights, predictions and automated prescriptions out of the growing data mountain. Yet, it is not a case of one size fits all. The technology needs to be available in everything from a free downloadable solution for developers to play around with on their laptops, to high-performance on-premise systems that can be housed in an organisation’s secure data centre.

 

They want answers

The highly-regarded 2018 Global Dresner Market Study for Analytical Data Infrastructure (ADI) revealed that on-premises deployments of ADI platforms was leading in priority over cloud deployments but that the deployment option varies wildly by use case. It did, however, note that respondents’ priority for hybrid deployment (a mix of on-premise and cloud) has increased year-on-year.

 

Wherever the ADI platform is deployed, one thing that all respondents agreed upon is that performance is a top priority for any embedded analytics. This is not surprising. Businesses today operate in rapidly shifting marketplaces, so agility is imperative to not just surviving, but thriving. To facilitate this, they not only want answers, but want them now.

 

The other consideration respondents cited for an ADI platform is its inherent security. With the EU General Data Protection Regulation (GDPR) just around the corner, the consequences of not properly securing data are bigger than ever. Due to come into force May 2018, the GDPR will mean that firms that suffer a subsequent data breach could face a potential fine of €20m or 4% of annual turnover – whichever is greater.

 

Horses for courses

While the popularity of the cloud has gathered pace in recent years, it is still horses for courses. Each organisation is different. Whether they prefer to host their data on-premise, in a private cloud, public cloud or prefer a hybrid solution, organisations need the right architecture for their specific data eco system. It should facilitate data storage, standard reporting and data processing, artificial intelligence and a flexible way of adjusting to future trends in an open, extensible platform.

With the siloed nature of data, the likelihood is that it will increasingly reside both on-premise and in the cloud. Therefore, it is important for businesses to be able to integrate and transparently access data sources in such hybrid environments. They need a solution that will allow them to set up one database cluster in the cloud, another one on-premise and connect both systems using virtual schemas, so that they have a 360-degree view of their data.

Tapping into insight

ADI is becoming a significant topic within business intelligence and analytics. Businesses have woken up to the fact that there is value in their data. We are now seeing organisations move to a place where business-oriented data strategies are a major focus. With that shift comes the need for sophisticated data science approaches that deliver swift results back to the business. With the right tools, they can tap into insight that improves their customer offerings, streamline business processes or reduce costs.

To better compete, businesses need to be proactively reactive. They are moving from an era of descriptive (looking at past trends), to predictive (looking to the future) and even to prescriptive (finding the best course of action to meet key performance indicators). To facilitate this, businesses need a powerful combination of the latest artificial intelligence tools and standard SQL analytics to create more agility and efficiency in finding the right insights out of data. The good news is that there are now platforms available that combine any data science language within the same system and combine it with standard database technologies.

 

Don’t leave your data behind

Savvy organisations today are transforming how they use their data, to unlock the power within. Whether that’s a multi-national retail business bringing together disparate data sources to mine for actionable insights to drive profitability, or a hand-to-mouth charitable organisation wishing to spot trends that could ultimately save lives.

As more and more organisations move towards a hybrid cloud concept that combines on-premise systems with public cloud deployments into one seamless IT landscape, it is important that they don’t leave their data behind. It is time for businesses to make an about turn and ensure that technology follows their data strategy, not the other way around.


Technology has to follow your data strategy

Sophisticated data strategies call for better analytical features and language diversity

Sophisticated data strategies call for better analytical features and language diversity

As we move into 2018, Analytical Data Infrastructure (ADI) is becoming a significant topic in business intelligence and analytics. Where Big Data was once an over-hyped, catch-all term, in the coming year we will see organisations move to a place where business-oriented ‘data strategies’ are the major focus. With that shift comes the need for sophisticated, yet easy to use, data science approaches that deliver results back to the business.

It is a point backed up by the 2018 Global Dresner Market Study for Analytical Data Infrastructure (ADI). The highly-regarded report revealed the key priorities for businesses for their data analytics and business intelligence efforts. From deployment to loading priorities, data preparation, modelling and management of data associated with ADI, the study captured the most important and current market trends driving the intelligent adoption of Data Science.

 

The Dresner report explored the ways in which end-users are planning to invest in ADI technology in the year ahead, along with the considerations behind implementation and use-cases. While security and performance were listed as the top two priorities for businesses, an interesting finding was that the biggest year-on-year change was the growing importance of easy access to and use of analytical features and programming languages such as the use of R, Machine Learning technology and MapReduce analytics.

 

Businesses have woken up to the fact that there is value in their data. With the right tools, they can extract that value – tapping into insight to improve the way they sell to their customers, or to streamline business processes and reduce costs.

 

But often, data has to be extracted, cleansed and transferred to other systems. In most companies, the Business Intelligence competence centres are separate teams to the Data Science teams, and they rarely work closely together. Modern analytics platforms combine these two worlds and allow to do SQL-based data analytics, Map Reduce algorithms and data science languages such as R or Python side by side. Many database vendors offer such capabilities, and some have even integrated these languages tightly into databases, allowing organisations to run data science on huge data sets.

 

While cleansing the data and finding the right models is a repetitive task that is sufficient to run on smaller data sets, high performance in-memory computing can make a vast difference when applying created R or Python models to billions of user data, in near-real-time.

 

Letting analysts use the data science tools of choice

Data analysts have their favoured analytics and visualisation tools which either leads to a wide spread of different tools that have to be integrated and maintained in the data management eco system, or to people not cooperating with each other. Further, the actual data science scripting language is often a personal preference. Each language has its own strengths and weaknesses in relation to the complexity of the task or features that the language offers.

 

As we move from an era of descriptive (looking at past trends), to predictive (looking to the future) and even to prescriptive (finding the best course of action to meet key performance indicators) for the most advanced analysis, the combination of AI and standard SQL analytics can create more agility and efficiency in finding the right insights out of data.

 

The good news is that there are platforms that combine any data science language within the same system, and combine it with standard database technologies. Exasol version 6.0 has for instance an open-sourced integration framework that allows to install any programming language and use it directly inside the SQL database. Pre-shipped languages are R, Python, Java and Lua, but you can also create containers for Julia, Scala, C++ or your choice.

 

Did you ever think it would be possible to provide normal SQL analysts access to data science results? Or that it would be possible to conduct powerful data processing in SQL rather than the programming languages? This leads to more flexibility, but essentially to exceptional performance.

 

Technologies has to follow your strategy

It will be interesting to see how data science technology evolves over time, and how companies move to leverage all possible ways of creating insights, predictions and automated prescriptions out of all kinds of data. This is not just a question of people’s skill sets or certain algorithms, but also the right architecture for your data eco system. It should facilitate data storage, standard reporting and data processing, artificial intelligence and a flexible way of adjusting to future trends in an open, extensible platform.

 

The technology should be available in the necessary ways – from a free downloadable solution to let developers play around on their laptops, to high-performance on-premise systems in your secured data center up to the standard public cloud platforms such as Amazon, Azure or Google. The technology should follow your data strategy, not the other way round.


Sophisticated data strategies call for better analytical features and language diversity