In today’s digital world, analytics plays an important role as an emerging competency with varied capabilities and has come a long way from traditional reporting and data presentations, thus transforming the technologies today into a more robust models, algorithms and data pieces. What’s more, with the evolvement of new Analytics, BI, Data Science, Data Management […]

In today’s business environment, we talk about data every minute as it remains the hot topic in corporate boardrooms, especially around Sales, Marketing and IT landscape. But the biggest challenge is what you are sharing and what it gives to identify opportunity and improve performance? And here comes the role of Analytics to drive data in the system, analyse and create segments as per the needs before coming across business insights and capabilities.

But we must understand that data alone is not the only solution. What is more important is how strong, how better and how faster the data can be collated to provide quality decisions and insightful analytics. And once data is collated, what steps are taken to harness the data. This is exactly why, the need for automated deployment is at scale. The data complexity and visualization has always been a challenge for executives and data management team and though Analytics has successfully provided the performance management and insights but lacked on some of the key areas including historical, current and predictive views of business, in short the total decision support system (DSS).

Today the time has come, when the two solutions must try and work in tandem: Web Analytics that analyses the interfaces and work around various reporting metricsand BI that analyses the flow of data that comes from the analytics.

Web analytics has always been the main source for driving all forms of data and it takes the help of data-warehouse to create and measure the infinite data. But with the change in time, more and more challenges started arising as businesses today are not only limited to complex data but are more open towards compounded data, and again data from various sources including direct and online. So to meet the increasing demand and get the transactional data easily flowing, we need the deployment of Business Intelligence. It helps understand how the web server’s data can be reaped to provide insights around actionable business intelligence, particular when the collaboration happened within various channels and processes.

The modern business supports the deployment of the two sources and provides a new platform in the organization, significantly reordering the vendor landscape. As per the Garter study, it is recommended that by 2018 most business users and analysts will move towards a new platform wherein they will have the accessibility for all the self-service tools to prepare data for analysis as part of the deployment process. The deployment might result in providing an end-to-end analytical platform or been integrated in the existing analytics processes.

What’s more, the study even emphasized that by 2018, Machine learning tools and Open-Source software like Hadoop together with search based and visual based data discovery will all merge to cover a single level platform that will cover self-service data analysis and natural language generation.

Here I want to point the initiatives taken by Microsoft (who has been recognized as 2016 Magic Quadrant for Business Intelligence and Analytics Platforms, for the ninth year in a row), on some critical areas of deployment (which ultimately can be said to be the ultimate stages for development). For them it’s a simple math, they took the initiatives around some of the strategic consent to get the processes up and running:

  1. Collaborated the entire BI platform with the data, systems and tools to provide one logical platform for all forms of analytics and data driven strategies
  2. Extended the existing BI & the reporting investments to the Cloud
  3. Focused around the built-in data modelling and advanced analytics to provide intelligent data solutions
  4. Focused around : “Write once, deploy anywhere”, including cloud, on-premises DB, Hadoop, Linux and Teradata Systems
  5. Accessibility around the ‘R’ scripts and models in CRAN (comprehensive R archival network)

Microsoft followed the Complexity v Business Value study in the entire automation sphere to touch some of the key attributes of business line.

All this helped Microsoft figured out the ways to get better in terms of analytical platform and helped them develop a foundation for all the networks under one single cloud. The deployment and the validation process was initially a challenge considering the lack of technical infrastructure it needed to deploy around the organizational units. It was also a challenge as the computations varied and the effort that requires to transform was not evaluated. But all forms of challenges looked feeble when the requirement was outlined.

  • Help increase the customer satisfaction by provide in-depth analysis, not limited to one platform
  • Deliver accurate forecasting and create a proper sales funnel
  • Predictive analysis to determine what’s down the road
  • Better KPI driven strategies and ROI measurement
  • Manage a proper channel between content, execution and strategies (Campaign has always been the prime focus)

The modern business arena has seen few revolution around the platform as they get ready to transform the big data to smart data and what is playing the key roles here is the strategies around cohesive growth for all three: machine learning algorithms, artificial intelligence and critical analytics & visualization infrastructure.

BI tools and Visualization

We need data, we need analysis and recommendation and we need a proper representation of the data as well. Here comes the role of Tableau and TIBCO, the leader in the visual representation of analytical data. The on-premise and cloud-based functionality just add an important angle to the organizational decision making as they can select the tools per their requirements.

BI + Analytics

If you want to lead the market and increase your sales, all you need is the best of analysis and implementation of key strategies which will again revolve around the analytical recommendations. But to run the compounded and advanced analytics including simulations, conversion rate optimization, predictive and sentimental analysis, you need the better implementation of BI together with adhoc analysis.

Hadoop and enterprise data warehouse integration

If web analytics and BI can provide you quality analysis and data, integrating Hadoop and BI EDW will help provide the complex and unstructured data to a more streamlined format thereby processing the data into the data cube structure using the BI tools. It is always important to understand the need and business value before actually going with such integrations as it involves some big decision making. But if integrated will provide some key benefits around real-time data streams, campaign performance, social media structuring and operational and financial analysis.

So why wait, deploy the two best models on-premises, in the cloud or in the hybrid environment to scale and transform the power of cohesive and un-structured data into more of business insights and intuitive reports by trusted analytical and BI capabilities.

I hope the article will provide some sort of understanding around the automated analytics and how the modern times has exponentially explored the integration. My next article will provide an in-depth understanding about the role an employee has to play in this environment and what it takes to make it big in this platform. Stay tuned and keep yourself updated with the latest in the domain.

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