Surviving the Leap of Faith

Strategies for small and medium businesses to benefit from data sciences, at the lowest cost!

Over the past few years, there have been quite a few instances of small to medium companies have been able to streamline their operations, introduce efficiencies and reduce costs by integrating data science into their decision making. However, a look at several recent surveys and reports shows that largely companies are still unable to utilize their data as effectively as they should. Even though there is an appetite among top management for using data in a more effective manner, a right data sciences strategy or the lack of it continues to pose significant challenges for most.

These challenges limit the organizations’ ability to leverage the right skills, tools & technologies to accelerate growth and are largely related to the following:

Data Availability & Quality

The first and foremost challenge lies with acquiring usable data that could be leveraged without much effort spent in improving its quality. In order to obtain the data, the user touchpoints should be designed to enable data collection. Appropriate design, data quality, compliance & storage considerations should be factored in while doing so. This may imply a need for redefining some existing business processes and re-building or modifying the technology components involved in data collection, cleansing, storage, and processing.
To mitigate this small to medium businesses today are looking at service providers who could provide them with actionable insights by leveraging whatever data assets they possess. Data-preneurs with flexi models are the need of the hour.

Cost of Technology Acquisition

To support the data science efforts, it requires significant investment related to multiple technical aspects, including network, user touchpoints (such as mobile apps, web, wearables, IoT devices & sensors), data cleansing tools, data storage hardware and software, analytics & reporting tools, to name a few. In addition, the cost of design and implementation adds to the overall cost of technology acquisition.

Given the above cost deterrent, it becomes absolutely imperative for small to medium businesses to look at a technology agnostic way of getting to the most optimal decision. Businesses just are concerned with the getting to the best results in a least cost way, no matter the technology.

Inability to Acquire the Rightly Skilled Resources

In addition to the investment in the technology acquisition, other vital element is the availability of the right skills. Building an in-house team of the adequately skilled people often turns out to be a long-drawn and expensive affair, which doesn’t guarantee results either. A viable solution to address the challenges in acquiring the skilled people is to hire freelancers having expertise in data sciences. You can use the CogniticX platform to connect with the real experts to mitigate such a risk.

Right skilling with each incremental functional requirement is a wishful thought. As a result, organizations may be saddled with skills obsolescence, ones that were expensive to acquire and retain in the first place. Further the same resources may lack functional understanding, which could be a critical differentiator. Circumventing the skills obsolescence challenge is a major hurdle in the successful adoption of data sciences.

Data security and privacy issues

While collecting data is vital to perform data analytics, it also opens your organization to the risks related to data theft, violation of data protection compliance and security attacks. This is why it’s very important to consider data privacy and security as one of the supreme requirements and enforce the required data security measures. These measures should be applied at various levels, including physical & online access to data storage, protection of data in motion, secure coding practices and adherence to the industry-specific compliance.

The Mitigation

Fortunately, there are mitigation methods available for small to medium businesses to navigate their way around the data sciences conundrum. The foremost is to look at flexi-leverage models such as those offered by Cogniticx, wherein one could engage a data scientist, armed with the right open source technology and skilled with the appropriate domain understanding to help an organization get to the holy grail, the most optimal decision, on demand.