Signals Through the Noise

Why a Platform?

Accelerate results for your business through a unified analytics platform.

Your organization processes massive volumes of data every day. Data about your business processes, your technology assets, your customers, your business partners and their customers, and even your competitors… it is all there. It is also anything but homogenous. Structured, unstructured, proprietary, third-party, log files, clickstreams, customer preferences — you seem to have more data types than the United Nations has flags.

You’ve invested in technology tools and highly skilled people who can help you figure out what the data should be telling you, and the team knows how to get to the right answer. The problem is that it seems to take forever to get there.

Your data analytics team spends so much time working through the mechanics of Big Data ingestion, preparation, and transformation that scarcely any time remains to conduct the analysis, and even less time is left to interpret the analysis before the next analytical project appears in your queue. Utilizing multiple analytical tools simultaneously creates additional complexity and delays. Complex data sets are difficult to enrich and analyze quickly, prolonging the analytics work cycle even further.

Somehow, all of this effort still isn’t generating the kind of business results that you had expected.

Do you need an analytics platform?

  • Business impact: Can I clearly trace my Big Data analytics efforts to revenue growth, profitability, and market share?
  • Return on data assets: How effectively am I extracting value from my data assets?
  • Time-to-results: Do my analytics activities require so much time that the analytics output drives far less business impact than we are seeking?
  • Throughput: Do my processes for managing analytics workflows limit the volume of business use cases that I can address with analytics?
  • Scale: Is adding staff members the only way to increase the volume of data that my teams can process?
  • Silos: Are analytics insights trapped within specific individuals instead of flowing freely to all business decision makers?

Simplify the complexity

Data science is a complex discipline, and that isn’t going to change any time soon. The technology that delivers the data science needn’t be complex, however.

As organizations mature, they typically amass a large portfolio of business intelligence and analytics tools. Some of these are features within large enterprise software suites, and some are standalone applications. Each purports to offer a fast path to business insight by identifying patterns in the vast quantities of structured and unstructured data that are often collectively called the “data lake.”

But how good is the business insight that you’re getting? Do you find that your analytics tools are generating visually attractive exhibits that don’t really help you make sophisticated decisions? If you are using tools that claim to use data science, are you spending so much time preparing data before the analysis that you hardly have time to focus on the analysis?

In either case, the complexity remains. There has to be a better way to drive Big Data analytics at scale.

Find the signal in the noise

We know intuitively that there is a “signal within the noise,” and the question is how to find that signal, extract it, and put it to work for the business.

A platform approach to analytics is the answer. And not just any platform. A platform that…

  • Allows analytics teams to rapidly ingest data from myriad data sources in whatever format the data exists
  • Integrates data from disparate sources into a consistent, unified view that facilitates efficient data management
  • Prepares and transforms data as an automated process
  • Extracts insights using advanced data science techniques, not traditional, basic analytics approaches
  • Enriches and refreshes the insights constantly via machine learning techniques
  • Packages insights for rapid and intuitive consumption in analytics workflows
  • Enables people in all functional roles throughout the business to easily access and apply the analytical output

Not all platforms are designed with this philosophy in mind. Many try to deliver this capability, but most fail.

One succeeds.

 

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