INTRODUCTION
With the high usage of data nowadays, Data warehousing is becoming an important aspect to store information. The major feature upgrades to software like SQL Server in 2012 and 2014 have meant that they can use column store indexes which makes things faster and better availability. Incorporation of social media information has become so important that enables analysts to determine behavioural competencies and design campaign and essentially. But capturing of social data information isn’t easy, because the data is not in a structured format. Alteryx is one such tool which can access large volume of data and much faster pace.
ALTERYX
Today’s analysts need to live at the forefront of innovation to keep their organizations competitive. Data blending helps today’s analysts take full advantage of their expanding roles, as well as the expansion of data needed to make those critical business decisions. Data blending is the process of combining data from multiple sources to create an actionable analytic dataset for business decision-making. Alteryx pioneers its work in the field of Data warehousing and Data Blending.
Alteryx has been a go to tool for mining social media data and visualize the information and provide true business insight. Social media channels, such as Twitter, Facebook, Yelp, Foursquare, and LinkedIn, provide a wealth of valuable information for forward-looking organizations, not only to better understand the past but also to predict the future. Integrating social media information also opens the door for organizations to understand the economic impact and value of social media activities, such as a Facebook ‘Like’. But doing so requires a holistic rather than a siloed approach to social media analytics. In isolation, social media can give an organization a feel for consumer sentiments and let it track the size of its social media “footprint,” However, it’s not possible to measure how that social media activity impacts sales and customer loyalty or purchasing patterns unless that data is integrated into and compared with product usage, retail outlet, loyalty card and other information.
Social media is only one example of the rise of non-traditional data sources categorized as ‘Big Data’. High-volume, high-velocity, and high-variability data—generated by electronic sensors, RFID systems, web servers, and cloudbased applications—are also becoming critical sources of intelligence for organizations. However, this data is often unstructured or semi-structured, and cannot be easily modeled or stored in traditional relational databases or data warehouses.
In order to harness Big Data for deeper insight, Alteryx enables organizations to incorporate unstructured and semi-structured data into their analytics, move beyond conventional sampling techniques, and build more robust predictive models that more reliably predict business outcomes related to customer adoption, future sales, and market growth. By allowing data analysts to easily access and integrate semi-structured data sources, Alteryx dramatically lowers the hurdle to driving business insight from unconventional data sources. Plus, direct integration with popular Hadoop-based and NoSQL platforms enables Alteryx to easily exploit new data platforms while reducing IT costs.
integration of R’s predictive analytics into the Alteryx platform also allows rapid, large-scale data access, the ability to introduce additional analytical capabilities, such as spatial analytics on top of predictive models, and the ability to easily move from a desktop model to an analytical application deployed in the cloud without needing to involve IT.
CONCLUSION
It’s long been said that change creates opportunity. Recent changes in the business, technical, and user landscape are creating tremendous opportunities for organizations to get a deeper understanding of their market and their customers and enabling them to make a radical, 180-degree change in how they make business decisions.