Data Science and Machine Learning are growing at a rapid pace as more and more companies, whether it is large or small, are seeking to expand their arms. But the primary adopters and implementers of these technologies today are majorly large enterprises and smaller businesses, because big organizations have much capital to invest, while smaller ones are unencumbered by long chains of command.

On the other hand, medium-sized enterprises are facing or having a harder time. As having not much resources like the bigger players or the agility like the smaller ones, mid-sized businesses are slower to adopt and implement data science and machine learning technologies. However, they need a smart approach in their efforts and if it happens, they can get the significant value of where they do invest.

Smart and effective approaches that businesses take are the most important things while implementing such technologies. Use cases here can make sense for the successful implementations of data science and machine learning tools. Because without knowing and identifying the suitable use cases, investing money, time, and technology on the company's issues will likely to fail.

As the growth of compute power continues, more enterprises are leveraging these technologies today. However, organizations also need to hire experts who have skills, knowledge or talents regarding the use of data science and machine learning tools.

According to a report from Dresner Advisory Services, basic technologies such as dashboards and reports remain a top priority for organizations, while neural networks may be big in terms of interest, but not in terms of actual deployment yet. The Dresner report surveyed 864 individuals at small to very large companies, including IT professionals, executive suite, and other business areas in organizations across the world.

The report further found that data preparation remains significant among survey respondents. Co-founder and Chief Research Officer at Dresner Advisory Services, Howard Dresner said the increased use of data science and machine learning has helped enrich the data that some of the more traditional business intelligence tools use. For instance, data professionals can extract the essence or add scoring and then add those values back into the database. The new insights can then be used by the Business Intelligence tools. The added compute power available today makes data prep easier for these purposes, and other purposes, too. So, data prep is more important than ever.

He further added that data prep is front and center for everything because of the variety of data formats out there. There's so much data available today, for free, in syndication, from government agencies. But you have to bring it together to create relationships where there are relationships.

So, data science and machine learning are having significant impacts on all-sized businesses, and are rapidly becoming critical for differentiation and sometimes survival.