Data science solutions could seem overly technical and complicated at times. AlgoTactica strives to communicate these concepts in a clear and straightforward manner that enables clients to feel comfortable and reassured about the solutions that we provide. If the FAQ’s below do not address your query, then please do not hesitate to contact us for a dialogue.
The field of predictive analytics uses mathematical and statistical methods that enable the extraction of information from data, thereby providing the knowledge to empower confident business decision making. The methods can organize enormous volumes of raw data and distill it into concise information components, that can then be used to minimize risk exposure and optimize business performance.
By analyzing the data, these methods can assign a predictive score for each business entity that is under study, including customer purchase propensity, future sales volume, credit default potential, etc. Typically, these scores are assigned by a mathematical model that has been trained on data containing information which reflects the previous business experience of the company. It is the scores that are then used to guide business decision activities.
For small business applications, it is possible to design an open source data analytics platform tailored to the specific needs of the small business, without incurring the huge software licensing costs normally associated with proprietary platforms offered by commercial vendors. The only direct costs involved would be associated with the purchase of computer hardware and the contracting of a data science consultancy following a firm fixed price quotation.
A low-cost data analytics system can be built that encompasses the following data management areas of practice, and their associated free-of-charge open source technologies:
Essentially, Predictive Marketing strives to anticipate what each customer’s next need/wish likely is based on an in-depth understanding of his/her relationship with your business. This is often described as having a 360 view of each customer.
The 360 view enables the business to segment and target by customers’ engagement history instead of by their demographics. It is particularly useful for running micro campaigns which generally yield higher ROI’s than conventional larger scale campaigns.
Data science implements advanced analytical techniques drawn from the fields of mathematics, statistics, operations research, information science, and computer science. The objective of the analysis is to discover data patterns that provide a better understanding of a business process, in order that future actions can be planned and executed with an optimized degree of accuracy. Generally, data science methodologies can be applied to data sets of any size, from very small to very large.
Big data embodies software technologies that are used to enable data science analytics when the data sets are too large for a single compute platform. In this situation, the analytics software partitions the large data set into an ensemble of smaller parcels, with each individual parcel paired to an individual computer, within a network of computers. The parcels are then all analytically processed at the same time, using a memory-efficient parallel compute strategy. Typically, the technology is also supplemented by specialized database systems designed to maintain integrity of the distributed dataset by coordinating storage and retrieval actions involving the parcels. These are often referred to as NoSQL database architectures, and employ table structures that are more flexible than those found in traditional databases.
Business intelligence is a much broader term typically referring to the overall process by which data is gathered, arranged in databases, analytically transformed for knowledge discovery, formatted for presentation, and then acted upon by the organizational end user. Big data and modern data science technologies are a part of this, but it can also include older technologies based on traditional data warehousing concepts. The approach can include datasets from separate knowledge domains, both internal and external to the organization, and utilize a broad combination of legacy and modern software technologies. These components are merged per a knowledge management policy defining how they must be organized to provide the required added value for optimized decision making.
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:
There are six main types of analysis that can be employed to extract value-added information from data. The list below presents them in increasing order of complexity. However, this does not reflect order of usage in any given study; in fact, most studies will only employ a few of the methods, and not all of them. The types are summarized as follows:
Big Data algorithms cannot be any more accurate than the data used to train them. If the data is sub-par in any way, then decisions that are made based on the analysis will be inherently flawed.
Assess the quality of your data based on these criteria:
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:
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.
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:
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.
Machine learning is a data science method that uses existing data sets to train software algorithms that can learn how to make predictions about future outcomes. During training, the algorithm will discover previously unknown patterns in the data that will enable it to construct rules as to why each event in the data set has occurred. Once these rules have been built, the algorithm can then be used to predict similar future events when used to process other data sets taken from the same business problem area. Machine learning procedures typically produce predictions that are of high value to business planning decisions in many areas of application, including the following:
For additional information on uses of machine learning, please see the FAQ answer for ‘What are some typical applications for predictive models?’
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.
Each engagement event between a business and a customer yields information that can be organized in a database, and subsequently analyzed to build an informative data model. A professionally designed data analytics strategy will model the events, analyze them to learn from past and present business patterns, and then use this prior knowledge to predict future trends. By using this information, it is then possible to design a business plan that anticipates evolving customer preferences and uniquely identifies emerging opportunities in a dynamic marketplace.
The initial stage of the data analytics process involves the development of descriptive models, which are used to discover previously unknown relationships hidden in the business data record. Based on the insights acquired, predictive models are then designed which utilize these newly-discovered historical patterns, to make forecasts that answer questions about future outcomes.
As awareness grows regarding the benefits of predictive analytics, these methods are being applied to an increasingly broad range of business-related problems. Development of big data software technologies, coupled with the availability of more economical data processing hardware, is driving the application of predictive modelling across many industries, including health care, insurance, manufacturing, retail, and numerous others. Here are several examples of how predictive analytics is typically being used in industry:
A model typically consists of a mathematical procedure designed to provide answers to a specific business problem. The role of a model is to operate on business data in order that relationships within the data are used to anticipate future outcomes. Data-driven business decisions can then be made by acting on the knowledge acquired from those predictions
In general terms, a model will make use of input variables that have the power to anticipate the outcome of some other variable that is dependent on them. As part of its design stage, a model will be subjected to a training process involving a data set that is used to teach it about relationships that exist within the data. During that training, the model will build internal mathematical rules that specify how it should formulate predictions when given new input data on which it has not previously been trained.
Once its mathematical rules are established, the reliability of the model is formally vetted by examining performance error measures and by further testing against additional data that was not used in training. This is done to ensure that the model can generalize its learned rules in a way that enables it to perform accurately when exposed to previously unseen data. In some instances, a model will learn rules that are not sufficiently flexible for generalization, and will exhibit very accurate performance when given data that was used to train it, but will show very poor performance on any other data. In such a case, the model is described as having been over-fitted, and will need to be retrained in a more careful fashion.
There are many ways in which data science models can be defined and categorized, especially with respect to mathematical complexity, time required for training, as well as underlying theoretical assumptions. An often-used categorization is with respect to the four main types of learning that can be used to train them:
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