The Business Case
If all of your data consisted of a single source of records that were complete and unambiguous, it would be relatively simple task to solve it.
In the real world, however, the picture is normally very different. Data is typically far from complete, frequently ambiguous, and often scattered over many different data sources, recording many different attributes with few overlapping fields. Collecting data from all the different sources into a single, central storage area is somеtimes impossible to complete it in a reasonable timeframe.
The Research Case
In this case we have company data from different systems and time periods. We need to find the model that assigns the same company ID to all the records of the same entity – the process that is called deduplication.
|555 1ST FRANKLIN FINANCIAL
555 1ST FRANKLIN FINANCIAL CORP
You could use all available public company information to add to the provided data sets and build reasonable algorithms to predict unique ID and assign it to all available records.
The ZenCodeo expert for the Datathon
Nikolay Kostov, Industry Expert, Solution Architect @ ZenCodeo OOD
Nikolay Kostov works as a Solutions Architect in ZenCodeo. He used to manage one of the most successful private educational institutions in Bulgaria – Telerik Academy. He studied Computer Science in Sofia University and now he finishes his master degree in New Bulgarian University. Few years in a row, Nikolay was awarded with Microsoft Certified Trainer certification. He also was awarded with “Student of the year in Bulgaria” as well as “30 under 30” of Forbes Bulgaria.
The main focal point for presenting the results from the Datathon from each team, is the written article. It would be considered by the jury and it would show how well the team has done the job.
Considering the short amount of time and resources in the world of Big Data Analysis it is essential to follow a time-tested and many-project-tested methodology CRISP-DM. You could read more at http://www.sv-europe.com/crisp-dm-methodology/
The organizing team has tried to do the most work on phases “1. Business Understanding” “2. Data Understanding”, while it is expected that the teams would focus more on phases 3, 4 and 5 (“Data Preparation”, “Modeling” and “Evaluation”), so that the best solutions should have the best results in phase 5. Evaluation.
Phase “6. Deployment” mostly stays in the hand of the case-study providing companies as we aim at continuation of the process after the event. So stay tuned and follow the updates on the website of the event.
1. Business Understanding
This initial phase focuses on understanding the project objectives and requirements from a business perspective, and then converting this knowledge into a data mining problem definition, and a preliminary plan designed to achieve the objectives. A decision model, especially one built using the Decision Model and Notation standard can be used.
2. Data Understanding
The data understanding phase starts with an initial data collection and proceeds with activities in order to get familiar with the data, to identify data quality problems, to discover first insights into the data, or to detect interesting subsets to form hypotheses for hidden information.
3. Data Preparation
The data preparation phase covers all activities to construct the final dataset (data that will be fed into the modeling tool(s)) from the initial raw data. Data preparation tasks are likely to be performed multiple times, and not in any prescribed order. Tasks include table, record, and attribute selection as well as transformation and cleaning of data for modeling tools.
In this phase, various modeling techniques are selected and applied, and their parameters are calibrated to optimal values. Typically, there are several techniques for the same data mining problem type. Some techniques have specific requirements on the form of data. Therefore, stepping back to the data preparation phase is often needed.
At this stage in the project you have built a model (or models) that appears to have high quality, from a data analysis perspective. Before proceeding to final deployment of the model, it is important to more thoroughly evaluate the model, and review the steps executed to construct the model, to be certain it properly achieves the business objectives. A key objective is to determine if there is some important business issue that has not been sufficiently considered. At the end of this phase, a decision on the use of the data mining results should be reached.
Creation of the model is generally not the end of the project. Even if the purpose of the model is to increase knowledge of the data, the knowledge gained will need to be organized and presented in a way that is useful to the customer. Depending on the requirements, the deployment phase can be as simple as generating a report or as complex as implementing a repeatable data scoring (e.g. segment allocation) or data mining process. In many cases it will be the customer, not the data analyst, who will carry out the deployment steps. Even if the analyst deploys the model it is important for the customer to understand up front the actions which will need to be carried out in order to actually make use of the created models.