As per a recent study , on an average an individual organization will spend about $7.4M on data-related initiatives over the next twelve months , with enterprises investing $13.8M, and small & medium businesses (SMBs) investing $1.6M. 80% of enterprises and 63% of small & medium businesses (SMBs) already have deployed or are planning to deploy data sciences projects in the next twelve months.
36% of enterprises expect their IT budget allocations for data-driven initiatives to increase in 2015, 41% anticipate budget levels will remain at current levels and 21% aren’t sure. Only 3% say data-driven and big data-related project funding will decrease.
Data sciences continues to accelerate as the most preferred solution for gaining greater business insight and value from data, with this category increasing in importance 55% from 2014 survey results. In enterprises, data sciences (65%), visual dashboards (47%), data mining (43%), data warehousing (40%) and data quality (39%) are the five most preferred solutions. In financial services and manufacturing companies, the shift away from pre-built dashboards with common metrics and key performance indicators (KPIs) to the flexibility of defining their own data models in metrics is the future. Dashboards in financial institutions need to have the flexibility of quickly integrating entire new metrics and KPIs as their business models change. For manufacturers, the need for interpreting shop floor data to financial results is what’s driving data analysis and dashboards in the many manufacturing industries adopting analytics today.
There has been an ongoing considerable shift in the budgets, reallocated to data-driven activities like advanced analytics, majorly to focus on creating business impacts in the following sphere:
Without data scientists and their knowledge, many issues surrounding the digital business age will remain unresolved — possibly even untouched. Data scientists frame complex business problems as machine-learning or operations research problems. Data scientists know which new information sources should be collected or acquired from external sources, to solve old burning business issues in radically new ways.
There are many more examples of disruptive projects and new “business moments” made possible through data:
Data scientists must engage with data expeditions, especially when there is no clear objective other than to explore the data for insights and tidbits. Such expeditions are a form of inductive thinking or inductive reasoning— an example of “letting the data speak.” The process can be tactical and ad hoc. Alternatively, it can be part of a more systematic practice in which you give the data science team a data dump for diving into and exploring. The lab then looks for anomalies, seeking something new. The most basic techniques are:
The objective is always to:
Data sciences and especially machine learning excel in solving complex, data-rich business problems where traditional approaches, such as human judgment and exact solutions, either increasingly fail or deliver inferior solutions (see “Machine Learning Drives Digital Business”). Data science methods have been proven to often deliver superior results, when the space of critical variables is highly dimensional and very noisy. Hundreds of new business problems exist that data science teams could tackle. Companies are already using data science teams for tasks such as:
Refinement — Continuously Improve Existing In-Production Solutions
Most data scientists in the industry work in the production part of the business. In such areas, established models are already “in production.” For example:
In all these use cases, organizations must constantly improve advanced analytics because:
Sometimes it may be almost impossible to avoid a crisis because insights into issues that may cause problems can be so well hidden. In such cases, use your data science team to help resolve the crisis. This use is a variation of the data expedition use of data science teams. Many data science projects are triggered by crises. When you ask a data science team in this way, you already know the “symptom” of the crisis. For example:
This means that the data science team has to identify “only” the cause, which narrows the datasets it must scrutinize. Everything else in this use scenario is very similar to the work the lab does in data expeditions. As in big data expeditions, the lab does not know at the outset whether it can identify the cause of the problem. Indeed, it is possible that the lab may never be able to identify the cause.
Basic data discovery/self-service business intelligence can often help. However, a deeper dive by a data science team can extract more from the data about what is really happening. For example:
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