Top Three Business Drivers as Enterprises Embrace the World of New Data

by   |   May 2, 2017 5:30 am   |   0 Comments

Anwesa Chatterjee, Director of Product Marketing at Informatica

Anwesa Chatterjee, Director of Product Marketing at Informatica

According to the recent State of Cloud Analytics 2016 report, cloud is a key — or at least a very important — part of enterprise analytics strategies for 91 percent of survey respondents.

Clearly, cloud analytics is well past the tipping point and is now the top-of-mind focus for most enterprises. But while deciding to adopt cloud analytics is one thing, making it work is altogether different. What obstacles are the 91 percent likely to encounter as they move to cloud, and how can they overcome these to maximize the benefits of cloud analytics?

To understand this, we first need to look at the factors driving cloud adoption so far.

Analytics is not new, and neither are a lot of the technology concepts underpinning the latest systems. As for the motivation behind achieving robust analytics, businesses are always eager to gain insights into their performance or uncover opportunities for improvement.

What is new, however, and what is driving more enterprises to the cloud, are:

– New data and data sources. Big Data, social data, machine-learning data and data from the Internet of Things (IoT) are just a few of the data sources that have emerged in recent years, and more are appearing all the time.

– New users. Instead of specialist business intelligence (BI) teams working with IT to source, wrangle and analyze data, BI has been democratized. Now, lines of business, such as marketing, sales, support, and research and development, all want real-time data and dashboards. In addition, this “self-service data” relies on the agility that comes from the cloud.

In response to this, enterprises have turned to cloud analytics because:

One of the cloud’s biggest benefits is its cost-effectiveness and scalability, so as data volumes continue to grow, it’s a natural solution. Early cloud-service providers like Salesforce disrupted the market with solutions that were more user-friendly than their on-premise predecessors, eventually enabling the cloud-based self-service analytics applications of today.

On top of the changing landscape, the cloud provides business benefits that are driving adoption. Specifically, enhancing business processes, providing a better customer experience and enabling better collaboration are the top three business drivers uncovered by The State of Cloud Analytics 2016 report.

Most businesses now have a mandate from the C-level to pursue digital transformation (through application modernization) and to develop new revenue streams, for which the cloud’s agility, scalability, cost benefits and security play key roles. What’s more, transforming the customer experience demands a more complete view of activity across social, mobile, product usage, buying patterns and customer-feedback channels, making on-premise solutions and their accompanying siloes impractical. The same is true for fostering collaboration—both among internal teams and with partners and suppliers—in a globalized economy.

What are the Top Three Hurdles to Succeeding with Cloud Analytics?

1) When looking to maximize an investment in cloud analytics, the first obstacle to overcome is fragmentation of the analytics stack and various point solutions. Cloud analytics is not one solution but a whole stack of solutions, from the data coming in—in a structured or unstructured format—to transforming and normalizing that data in the middle layer, and then on to using that data by the report engines in the top layer. Viewed from end-to-end, the stack is a whole series of point solutions. If not handled carefully, the ever-increasing total cost of ownership for managing and integrating data across the various point solutions can be a threat to cost savings.

2) The middle layer of the cloud analytics stack is where the second challenge sits. Data management and governance is critical, yet many times overlooked, because of the absence of the right platform and tools. This oversight is something we’ll revisit shortly.

3) The third obstacle is the inability of current systems to reuse the required existing skillsets and resources to manage and operationalize an effective cloud-analytics strategy and platform.

 One Platform to Rule Them All

The middle of the stack is best-equipped for managing and transforming data, so that it can be used by all systems and all users. It can also mitigate the effects of fragmentation and, over time, help address any skills’ shortages.

Because of the organic nature in which data-analytics initiatives have grown—and other factors such as merger and acquisition activity—it’s currently not uncommon for organizations to have different teams maintaining different solutions and integrations in this middle layer.

This leaves a gap in collaboration, where team and individual skillsets develop in isolation of one another, rather than in a manner where skills can be re-used in pursuit of one goal. In addition to this lack of collaborative effort, this setup just doesn’t work: ask the 61-percent of marketers who said integrating data across platforms was a significant barrier to their success.

Different data sources have different needs, but a holistic platform that covers them all gives an organization one platform, one vendor and one focus area for upskilling along with sharing experience and knowledge.

What Might the State of Cloud Analytics 2017 Look Like?

In the upcoming years, cloud analytics will see trends and challenges emerge around consuming and analyzing data from ever more emerging data sources, such as streaming, IoT, Virtual Reality and other connected devices. Another critical need could be around harnessing and understanding the metadata, relationships and data flow across various enterprise applications via business-user friendly tools. We will also likely see more analytical and data-management platforms built on robust frameworks using modern technologies, like machine learning and artificial intelligence, which will help business users easily search enterprise data-assets as seamlessly as Google searches.

What is certain, though, is that for organizations to succeed in building out a successful cloud analytics framework, they need to start with a solid data-management strategy. Cloud-based data management platforms will play a critical role in ensuring the delivery of clean, connected and trusted data through self-service analytics for all types of users, confirming cloud’s dominance of the data-analytics landscape.


Anwesa Chatterjee is the Director of Product Marketing for Informatica Cloud focusing on messaging, sales enablement and go-to market activities for the Cloud Analytics initiatives. She comes from Business Intelligence, Analytics and Big Data background with a keen interest in new technologies in the space. She has an experience of more than 15 years in various tech companies like SAP, Lockheed Martin, SugarCRM, and various startups in various roles ranging from Product development, alliances and product marketing. She brings a balanced acumen of technical and marketing skills to understand and position solutions to help customers better in today’s ever-changing complex IT.


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