Data does not equal knowledge.

Despite all the machine learning hype, today’s systems are basically only trackers of unintelligent process.

To realize the immense promise and potential of analytics, a fundamentally different approach is needed. And that requires that analytics providers dedicate their focus to business users and their domain-specific questions before systems, tools, and data integration are even brought into consideration.

Start with business leaders and their questions

Business analytics are about finding answers, discovering new insights, linking insights to decisions, and modeling the probabilities of the future — all with the aim to shape business strategy in such a way that it drives business performance. All of this starts with industry- and domain-specific questions that are directly tied to business performance.

Once these questions are known, the process of identifying, integrating, and qualifying data sources becomes more focused. It becomes faster, easier, and more cost effective. And it becomes far more actionable for the user and more valuable to the business.


Is the amount of variable compensation paid directly correlated to the bottom line results?

How does time in position relate to the first-call resolution rate of contact center specialists?

How can we hire and develop the right talent to drive product innovation?

Using human resources as an example, these questions may include:

Do stores with longer tenured employees have higher sales or better customer satisfaction?

How is caregiver engagement related to patient satisfaction — and how can we improve both?

Do more training hours result in fewer work-related injuries?

With traditional approaches and tools attaining answers to questions like these can take many months.

But to realize the full value of business analytics, answers must be delivered instantaneously and iteratively — without a reliance on IT specialists or data scientists.


Combine technology with business domain knowledge
The birth of applied business analytics combines domain expertise with technological utility.

Like an electrical outlet in a wall, decision makers should be able to plug in and immediately receive answers.

Decision makers should be able to walk through a process of discovery by iteratively asking business questions and attain contextual, domain-specific insights instantly.

This is in stark contrast to traditional efforts, where corporations have attempted to build their own data warehouses and analytical power plants only to realize
that a key data component is wrong or missing or that business people may not know the question they need to ask till they see a leading indicator.

The cloud makes this new utilitarian approach possible and economically very attractive. Business analytics can now be delivered as a service, with everything
provided. This includes the back-end infrastructure that handles all data ingestion and processing, as well as the shared, constantly evolving analytics application
— and can be done in a way that also protects security and privacy.


Feature 3

Modernize the Enterprise Software Business Model
The ability to develop and provide complete, domain-specific business analytics solutions in the cloud — prebuilt with best practice questions and connected to any data source — allows the enterprise software business model, including software-as-a-service approaches, to be completely rethought.

Instead of the traditional business model, which has organizations being continually hit by an avalanche of statements of works and devel-opment costs, the new approach to business analytics allows businesses to subscribe to a complete, pre-built solution — with all data management included — for a fixed and predict-able fee.

The result is that business analytics can be accessible to all enterprises regardless of their data maturity, and, specifically, all business leaders in all domain areas, from Human Resources to Marketing to Sales to Operations to Finance and beyond.

 

Leverage the power of thousands
The shared use of a domain-specific business analytics solution by thousands of users constantly improves the accuracy and sophistication of the domain-specific content. A new metric, a new way of linking results to policies, a new statistical technique, a new model can be virtually instantly adopted, generalized, market tested and deployed to everyone. In this way, human-refined machine learning has become a reality.

This means corporations can upload their data sources and start getting answers based on the most current best practices immediately. As part of a subscription-based model, businesses can finally begin to pay for results and insights — not for technology tools and IT services with their inherent high cost and failure rate.

The new approach to business analytics allows organizations to plug in and receive power, instead of trying to build their own power plant.


Outsmarting and outperforming with applied business analytics
 

This new approach allows organizations to get smarter. It gives them a better understanding of what is working and what isn’t. It shows them cause and effect, how variables are impacting business performance, and the probable outcomes if those variables are adjusted. It provides for real-time learning and instant adjustment, not needing to wait for the next planning cycle.

This is the rise of applied business analytics.

The transition is one from fear of data to clarity, from guessing to confidence based on statistics. It is the evolution from dependence on IT skills to self-service, from preoccupation with process to focusing on outcomes. It is the progression from wasteful spending on custom services to real bottom-line measurable value.

And it is attainable today.


NHI’s Vision is Working
When publicly-traded NHI customers are compared to all U.S. public organizations, NHI customers achieve more than triple the average return on equity.

NHI’s total cost of ownership is typically 1% the cost of building a custom solution — and organizations can be up and running in less than eight weeks.