4 Ways to Succeed with Analytics-as-a-Service

by   |   August 21, 2015 5:30 am   |   0 Comments

Clayton Weise, Director of Cloud Services, Key Information Systems

Clayton Weise, Director of Cloud Services, Key Information Systems

Big data has changed the way business is done. Businesses know they need to dig into their data to unlock the efficiency and improved services buried in that information. The value of applying data to operations and business processes has resulted in competitive advantages and changed some industries entirely.

But unlocking the value in data isn’t easy. From professional sports to waste management, and public transportation to Web security, the availability of more information and the technologies that store, manage, and analyze it has resulted in a computing landscape often bogged down by data. The headache comes from the management and analysis of that data on IT infrastructures not designed to handle these workloads.

There is a remedy for companies, however, with Analytics-as-a-Service emerging as a viable option for any organization that needs big data management and analysis without the capital expenditure necessary to keep those tasks on-premise. This means greater opportunity for analytics providers that can stand out as true partners for companies considering this investment. The following four tips can help Analytics-as-a-Service providers become a valuable proposition for enterprises.

Know What Works for Your Customer – and What Doesn’t

Analytics vendors know the end game – valuable, actionable insights that customers can use to work better and faster. There are critical decisions to make beforehand, though, to deliver the best experience to customers. Each aspect of this must be considered and agreed upon in line with customer expectations to ensure the right data is used to develop the best insights.

First, vendors must make clear which kinds of data are most likely to support analytics that companies can use, and know which data they are receiving. The first inclination for many customers may be to use it all and hope to get something valuable at the other end. Analytics providers need to help customers with clearly defined and explained parameters for the kinds of information that will best suit the project. It doesn’t stop there. It’s critical to define and agree upon, before any work begins, the process of moving the data from customer to analytics provider and back. Encouraging open standards ensures customers that they will be able to access their data throughout the process and that the data remain available for them to take and move as they see fit. Analytics-as-a-Service providers must make sure customers know that they can pull their data out, if needed, should they want to manage a portion of the project internally or end the project.

Related Stories

Insights-as-a-Service Grows with Focus on Real Time.
Read the story »

Data as a Service and the Analytics Hierarchy of Needs.
Read the story »

Improve the Customer Experience with Data Science as a Service.
Read the story »

4 Ways to Visualize the Value of your Unstructured Data.
Read the story »

Defining these standards early gives customers an understanding of the process and avoids any issues as the project evolves over time. Vendor neutrality is a concern for organizations working with new service providers. It’s imperative that analytics vendors give customers unfettered access to their data as they need it. No one wants to deal with vendor lock-in, especially when it comes to their own data.

It is equally important to agree on file formats. XML, JavaScript Object Notation (JSON) and other open-standard methods of encoding information make it easy for customers and providers to share information. On the other side of the relationship are cost considerations and the ability for customers to scale services up and down as they go. Cloud offerings need pricing models that ensure customers pay only for what they use. This encourages transparency throughout the process.

These considerations aren’t in place to outline any limitations. They’re merely designed to help all parties understand what’s expected for a mutually beneficial relationship.

Make the Right Connections with Customers’ Internal Teams

One of the biggest challenges in earning buy-in for an analytics project is selling the shift to chief technology officers (CTOs) and other decision makers. The member of a customer team most likely to see the need is someone with responsibilities and training directly related to business intelligence and data management. He or she understands the struggles that accompany the storage and analysis of data, as well as the limitations presented by the existing IT infrastructure. Common selling points include eliminating the need to upgrade an infrastructure, additional expertise from an analytics vendor, and vendor neutrality. Frequently, businesses that need analytics don’t require the necessary computing power every day, so working with a partner that can offer compute power as needed is a major cost benefit. Similarly, IT infrastructures frequently aren’t built to analyze the amount of data required. Working with a service provider gives businesses access to the computing capability they need without substantial capital expenditure. A vendor-neutral approach to the extraction of data will also put decision makers and other customer contacts at ease, knowing they can access their data as needed.

The customer contact who appreciates the real value of working with an analytics provider often needs some ammunition to convince superiors. When a service provider identifies an advocate within a company, it’s critical to convey these points to get buy-in from the people who must approve any investment. Moreover, analytics providers should have internal expertise in the common pain points their services can address within companies of varying sizes and industries.

Demonstrate Value One Layer at a Time

Once a relationship is established with a new enterprise customer, the first major barrier often is identifying data’s access point. Finding a seamless way to access and move the data and then analyze it presents a provider with an experienced extension of an existing team. Simply looking at servers and infrastructure won’t result in the holistic understanding of data required to generate real, actionable analytics. Accessing data on an application level and developing a process for moving it back and forth will quickly show customers, especially the skeptical CTO, that the relationship is working even in its earliest stages. This is especially true when enterprise customers have loads of unstructured data to process. Customers may view unstructured data as a lost cause. Service providers that can mine that information and provide trending analysis can demonstrate real value to the enterprise.

Some customers will work with providers solely for the computing power and storage of the data, but the analytics provider can also provide value by calling on their expertise to discuss the best ways to organize or manage data to get the best results from the project. It’s critical that service providers understand who is reviewing the data analysis. Whether it’s an internal data scientist or a business line manager, the analysis needs to be tailored to the person reading it. It’s not enough to receive raw data analysis – there must be a point to it. It won’t be difficult for customers to see the value if data is presented both ways.

Know which Customers Will Make a Winning Proposition

Part of any hesitance about key decision makers’ buy-in is the infrastructure and data services required for the project to succeed. Organizations that need this support from their analytics partner are most likely to be valuable customers. They need help and are open, maybe after a little nurturing, to identifying the best process to get the insights they need to improve their business. Support for the infrastructure necessary to develop analytics on the scale most companies require and with data they need to manage and extract makes a partner ideal in its earliest stages. Customers that want this level of support are likely to continue working with an analytics provider over time to add even more value.

For businesses, analytics are about one thing: getting better. Service providers that understand the ways in which customers want to improve and provide the path to make those adjustments are most likely to develop successful partnerships with customers. As infrastructure and data services fall into place, providers can start to move up the stack, collecting stored data throughout different applications – even unstructured data housed on individual computers and drives.

As more companies look into analytics, service providers that find different ways to drive value and deliver results to customers are most likely to stand out as ideal partners. The types of businesses looking for better ways to analyze their companies and become more efficient are likely to become more diverse. Every company needs better information and better access to critical insights that transform operations positively. Analytics vendors with a strong understanding of customer needs and a clearly defined value chain stand to capitalize on the trend.

Clayton Weise is the director of cloud services at Key Information Systems, where he is responsible for designing, architecting, and implementing cloud solutions; managing production workloads; and leveraging cloud resources in disaster recovery, clustering, and hybrid (cloud and on-premise) infrastructure solutions.

Subscribe to Data Informed for the latest information and news on big data and analytics for the enterprise.





Improving access to data across your company/partner ecosystem



Tags: , , ,

Post a Comment

Your email is never published nor shared. Required fields are marked *

You may use these HTML tags and attributes: <a href="" title=""> <abbr title=""> <acronym title=""> <b> <blockquote cite=""> <cite> <code> <del datetime=""> <em> <i> <q cite=""> <s> <strike> <strong>