Developing your data science analytics is critical in keeping your business model competitive.

Competitive business models hinge on the maximization of data’s potential.

Today, 90% of corporate strategies explicitly mention information as a critical enterprise asset and analytics as an essential competency.^[1]

By 2025, 80% of data and analytics governance initiatives will focus on business outcomes rather than data standards and will be considered essential business capabilities.^[2]

With this in mind, the future of analytics tools will be one that connects your decision-makers with their data analytics in the least amount of time and can produce models at the speed of business. This may be especially evident in e-commerce projections: through 2025, 80% of organizations seeking to scale digital business will fail because they do not take a modern approach to data and analytics governance.

By 2024, 30% of organizations will invest in data and analytics governance platforms, thus increasing the business impact of trusted insights.^[3]

Coming Up to Speed

Think of your company’s data like an oil field.

Data’s value is derived from its representation in modeling, but models are only valuable if you can extract the data properly, and efficiently, and deliver results into the right hands.

How you gather your data is the oil derrick.

The data modeling platform is the refinery.

Getting the insights to the right location is distribution.

The data-informed decision is consumers fueling their cars, liking their car’s performance, and coming back for more.

Much like oil dependency, the reliance on data is not going anywhere soon, and rightfully so: your company’s critical decisions hinge on the correct interpretation of available company information. This dependency is set to only increase.

Therefore, the technology your company chooses to process and model your data directly impacts the value your company ultimately derives from your market.

An article by Lee, Cheang, and Moslehpour contains a table of “[recent] studies of predictive analytics (PA) in various fields and the tools used” for further research.

Figure 1. typical operational capacity of a PA tool.^[4] https://towardsdatascience.com/5-steps-of-a-data-science-project-lifecycle-26c50372b492

Tactical Considerations

Deploying a data science strategy requires specific strategic and tactical skills, and an intelligent sequence to produce desired results.

The three major parties involved in a business’ data science pipeline are:

1. The Executive – focused on the corporate objective
2. IT personnel – focused on keeping systems safe and running
3. The execution team – focused on the tactics to achieve the executive’s direction.

Each level of the analytics chain holds its own intricacies.

• Action-oriented executives may want expedited results crafted at and with their own volition.
• Project importance is the deciding force in managing priorities – the “ad-hoc” projects can be executed immediately. Longer timeline projects may be kept in the queue.
• Frequently-shifting priority levels can diminish analyst effectiveness on a given project.

The ultimate value of an analytics platform is measured by its model’s insights. Further, insights must yield tangible and valuable action items that reach the proper personnel in a timely manner.

The delivered information must be digestible in an executive summary format, and the information must describe what will happen next quarter, year, and cycle. This allows executives to constantly refine their business model.

What to Avoid in Traditional Analytics

Executives should aim for a predictive tool or service that shortens the time between conceptualization and action. Here are the most common pitfalls avoided when adopting personalized predictive tools.

Technical/IT team queues

Executives should avoid submitting a work order ticket to an IT department, and should discuss their requirements with a data scientist or consultant to get the predictive analytics right the first time. Executive decisions take priority in an IT work queue and should not compete with other departments.

Educating staff (providing background and context), liaising

Project momentum is lost in email chains between staff and IT teams. The subject matter expert should have the capability to be as hands-on to the project as possible.

Slow pivoting

Making a change to your model should be as easy as clicking a button. As with the aforementioned reliances, actionable changes must happen rapidly to keep decisions – and therefore a business model – nimble. Each component of the data modeling process should be able to be tweaked in a matter of minutes. A highly user-friendly interface is optimal.

Delayed action correction

The actions you take are what derive the most value from a predictive analytics platform, but, similar to model nimbleness, sometimes the actions become obsolete as needs change. Last week’s models may not apply to this week. That’s okay, so long as inputs are quickly adjusted to make that action pertinent to your desired outcome at the time you need insights.^[5]

Misplaced dependency is the common theme in these hindrances. The best way forward for data modeling choices will be those that rely on as few links as possible. Thus, the tools and services you employ must be accessible to the subject matter experts who best understand your goals. Companies can therefore save the most time and human capital by selecting highly customizable predictive tools that intradepartmental decision makers and their subordinates can easily access and operate.

Capital Considerations

Cost factors of implementing a predictive tool include the number of use cases employed, number of users, storage usage, and analytics sophistication. A company can expect to conservatively pay between $6,000 to $48,000 per year for a predictive tool or service.

Implementation brings another cost in the form of human capital and time. Replacing one analytics tool with another is arguably easier if a team is thoroughly tech savvy. The investment steepens when adopting analytics for the first time, as this can require months of adaptation and even hiring specialized personnel. The dependencies of conventional analytics avenues and the array of current user-friendly technology should be heeded at this point: the most efficient tool selection is that which brings the most technical capacity and is fortified with the most user-friendly assistance.

The most cutting-edge platforms remove coding dependencies and assist users by anticipating their needs through pattern analysis and automation accomplished through machine learning and AI features. These platforms are on the pricier side of the curve.

Deployment

Vendors will go to great lengths to ensure the adoption of their product is as smooth as possible, but the level of involvement tech companies have in the process is the client’s choice. Choice of the server is the most topical distinction between types of deployment, as clients may have data security policies that limit where their data can be housed.

On-premise deployment is the most intimate implementation of a predictive analytics platform, where the software is deployed on a client’s server. The client retains full access to the platform’s permissions. The client hard-wires their data and models of their choosing into models.^[6]

On-demand deployment is accessible via cloud login. Data warehousing is often provided via third-party hosting. Clients have tiered access to the tool’s features and functions. Tool assistance and training are provided by request via a maintenance agreement.^[7]

Commonalities between deployment options are the vendor maintains the features of the product, and provides product training and support to client personnel. The vendor also retains licensing of the product.^[8]

How Will You Adapt to Quickly Evolving Data Trends?

Data continues to provide decision-makers and their teams with the crucial information that shapes their choices, and the tools to help them do so are increasingly outcome-focused.

A strong reliance on data science refines the business model of a forward-thinking executive.

References

[1] https://www.gartner.com/smarterwithgartner/why-data-and-analytics-are-key-to-digital-transformation

[2] ibid

[3] ibid

[4] https://towardsdatascience.com/5-steps-of-a-data-science-project-lifecycle-26c50372b492

[5] https://nemesisbyaviana.com/

[6] https://nemesisbyaviana.com/

[7] ibid

[8] ibid