Businesses today are routinely acquiring vast amounts of data, often from a broad range of sources. This presents tremendous opportunities for gaining unprecedented insights into matters critical for growth and profitability. However, traditional analytics cannot always accommodate the extremely large scale and rapid prototyping needed to get the strongest competitive advantage in the shortest amount of time possible.

Predictive Model Factories upscale the capabilities of big data analytics by enabling an extremely large number of individual models to be simultaneously trained in an automatic manner, without operator intervention. This permits a much greater number of models to be produced without requiring extra resources, and offers several benefits:

• Model retraining cycles can be iterated much more frequently, especially during overnight sessions when computer resources are otherwise lightly used.
• Allows a business to develop a separate model for each customer, to predict future buying preferences or timing.
• In a majority of cases, models for newly acquired customers can be initiated and trained automatically, without any manual intervention whatsoever.
• This also ensures just-in-time data discovery in very large and continually refreshed data sets, allowing the analytic model to learn, adapt and maintain predictive accuracy as new data is added.

As an example, Cisco Systems has deployed propensity-to-buy (P2B) models on a modest compute cluster of 4 computers, with 24 cores overall and 128GB of combined memory. This basic arrangement can calibrate 60,000 P2B models in a matter of hours, representing an overall efficiency gain that is 15x faster than their traditional methods.

In business, relevance and agility to adapt are key success drivers. Parallel computing and on-demand processing power enable a business to easily integrate these insights into the business workflow, and thus compete successfully.

Organizations that have previously realized value from using spreadsheets, and other similar small-scale analytical methods, will ultimately develop a need to accomplish even more with the data sources available. As they aim for higher accuracy, faster processing, and specific insights, the following are indicators of the need to move towards using structured big data methodologies:

• It takes too long to generate the desired ad hoc reports because volume and velocity (speed of arrival) of data overwhelms the current processing system and analytical methods.
• Standard spreadsheet applications can no longer address the business questions with sufficient granularity, specificity, or timeliness.
• There is a need to combine data from separate silos of different formats, different granularity, and different sources, to form multi-structured data sets.
• When unstructured data is to be mined for sentiment content, such as text messages, comments from social media streams and contact log/notes/recordings from the call centre.

Overall, you make the shift when there is much more data than can be managed by your existing software tools. From realizing this need to establishing an in-house team, there is a transition period during which it is best to seek help externally from seasoned experts. Smaller businesses may find it more cost efficient to continue to use external providers.

A data science solutions provider specializes in delivering highly effective business advice based on data-driven insights, derived from the use of predictive models and analytical software technologies.  The provider employs specialized technical skills and domain knowledge, to identify big data opportunities that are aligned with the business goals of the client. During this process, advanced data science techniques are used to discover patterns in marketplace dynamics and customer behavior that enable the client to anticipate future business opportunities. Using this prior knowledge, the client can then execute actions that will strategically position their business in order to exploit these opportunities for optimum profit and competitive advantage.

The expertise offered by a data science provider typically involves a combination of skill sets, including business and marketing science, software design, database development, statistical analysis, and machine learning. By collaborating as a multidisciplinary team, the resident specialists typically engage the following activities that are focused on delivering maximum value to clients:

• Design and implement machine learning software algorithms that are programmed to mine the big data stores and produce a detailed analysis for vetting by business and marketing experts.
• Apply marketing and business expertise to interpret discovered trends and develop a data-driven business strategy for clients, which will enable them to leverage maximum benefit from findings.
• Implement data hygiene protocols by cleaning and validating the data to ensure that it is accurate, complete, and internally consistent with respect to original collection methodologies.
• Engage data exploration and interpretation to identify analytics opportunities and insights that offer the greatest benefit for the client, and apply data-driven techniques in developing solutions.
• When dealing with limited data, apply statistical methods to identify business questions that the available data can answer, thus maximizing the information potential for the data set.
• Collect large sets of structured and unstructured data from various sources, identifying and reformatting those of best quality, and then determine the variables with best predictive power.
• Design Java software architectures for managing distributed computing and database storage applications, and for networked communications involving data feeds from real-time sources.
• Maintain comprehensive knowledge of a broad range of statistical and machine learning software tools, so that blended solutions can be designed using the best advantages of each tool.
• Develop data visualization software strategies that intuitively communicate important findings to stakeholders from various non-technical backgrounds.

Metadata refers to small granules of data used to capture basic knowledge that provides a descriptive overview about very large volumes of data. It is higher level information that is used to organize, locate, and otherwise manipulate extensive data sets when it is not practical to work directly with that data itself. Aspects that metadata can summarize about large data sets include structure, content, quality, context, ownership, origin, and condition, amongst others. Because the metadata is much smaller than the data it describes, it acts as a search index to facilitate quick identification and retrieval of archived data sets that are being sought for a given business objective.

In the modern era of big data, the role of metadata data has become much more critical than it has been in the past. It is now very important for businesses to manage the continuously-growing volumes of structured and unstructured data, in order that they can efficiently leverage it to maintain competitive advantage. For instance, semi-structured and unstructured data is often spread across many different storage devices and locations, can be stored in a diversity of formats, and is difficult to organize overall. Consequently, cost-effective usage and management can only be achieved if there exists a metadata oversight program, that minimizes the time and expenditures associated with discovery of the relevant in-house sets of big data.

As data volume and diversity continues to grow, for each new big data project that is launched there will be an ever-increasing efficiency imperative that the relevant data sets be identified through searching of a descriptive and accurate metadata layer. In fact, at the final reporting phase, metadata summaries can provide an audit trail to authenticate the quality of source data from which the analytic findings are drawn.

Many large data science providers are focused on offering prepackaged solutions which involve combining commercial-off-the-shelf (COTS) analytic software products offered by multiple third-party vendors. Depending on the stated needs of the client, this might involve delivery of an analytics platform based on well-known COTS software components, delivery of COTS distributed database systems to replace existing legacy relational databases, or some other similar combination. In all these cases, the larger provider is actually performing in the role of a systems integrator, as opposed to an OEM provider of custom-designed analytics solutions.

Although the large provider might well have data science knowledge in-house, when it comes to addressing a client’s need, that knowledge is likely to become focused on identifying prepackaged software that will ultimately prove to be only an approximate solution and not necessarily the best fit. Given the scarcity of available professionals with advanced data science skill sets, large providers that are focused on maximizing sales volume cannot acquire a talent pool sufficiently large as to facilitate detailed investigation of each client’s specific data science need. This ultimately leads to proposed solutions that are commoditized to appeal to a large range of clients, even though the solution might not be precisely conformant with the exact needs of any individual client.

At AlgoTactica, we focus on investigating each client’s data problem in detail, and then proposing the appropriate approach that will yield the optimal solution. After an exploratory data analysis (EDA) stage, we engage a scientific due diligence process during which a uniquely relevant set of algorithmic candidates are evaluated against the client’s data to identify the best one. Once identified, we can quickly build a one-of-a-kind customized product by compiling software from our in-house mathematical algorithm libraries. Therefore, the ultimate solution is designed specifically to the needs of the individual client and will not attempt to be an omnibus solution for a commoditized marketplace.

The principals at AlgoTactica have graduate degrees in specialized fields of marketing science and engineering mathematics.  Furthermore, we have decades of combined experience in market development, design of analytics software, and data science involving machine learning and statistical analysis.  We have built our professional careers by maintaining an awareness of the latest innovative advances in our field, and then leveraging those innovations for delivery of highly-customized solutions to well-known industrial brand names.