Presentation and Visualization Methods

Presentation and Visualization Methods

The era of today’s technology and corporate culture has a lot to deal in terms of the data that needs to be analyzed. In addition, time constraints are applicable on almost all the professionals. This calls for coming out of the conventional way of analyzing figures and numbers and stepping in for visual depictions that makes the interpretation of events easier and quicker. The specific visual, however may be good for some and otherwise for others. The intended audience and their requirements need to be paid attention to first. There exists a lot of effective presentation and visualization methods for various business vignettes such as telecom bills, insurance premiums, order summaries etc. Though almost every technique can be utilized to represent any of these, there exists special visuals that outweigh any others with respect to the appeal and easy comprehension by the audience who views it. Whereas bar charts may be sufficient to view the time trend of a particular quantity (ex: demand for a product), other visuals such as heat maps, scatter plots, dashboards are equally important in other domains of relevance.

We choose the following business vignettes to describe the variations of visuals that may be used to represent the relevant information to the intended audience.

·       Retail sales

·       Financial Services

·       Insurance

   For each of the vignettes mentioned above, the following content elaborates upon the requirements expected out of each of them followed by the recommendations on best visuals and sample depictions.

1.    Retail Sales:

The audience for viewing reports on retail sales varies by size of the organization as well as role of the people in respect to the organizational hierarchy. Big organizations’ management desires a global report with a granularity of region/continent level. People lower in the hierarchy need it to be more granular in terms of both timeline and regional breakup. The sample chart depicts the markdown in terms of store and product category at a specific regional level. The report further highlights the absolute pound amount on right hand side. Along with the bar charts, outliers are highlighted in the charts that reflect on aggregate measures, ones that may be of interest to the management in the organization.

2.    Financial Services:

The financial organizations such as stock exchanges, bullion exchange need to analyze the data from a multitude of angles. Whereas some may like to monitor stock prices of a company over time, others might want to explore the trend in overall market index. Currency monitoring is another facet where major currencies of the world are monitored on price relative to US dollar amount. The sample cited above uses line graph to monitor the currency prices over time. The report also has a zoom-in feature to dig into granularity of hours and minutes in a day. Scatter plot depicted on the right depicts the trend relative to the percentage volatility in a currency for a particular period of time. This kind of report is routinely used in institutions such as stock broking firms, equity banking firms etc.

3.    Insurance:

The management in automotive insurance companies are interested in improvising the probability model upon which they charge premium on various products offered. On these lines, they often wish to analyze time series data of the number of accidents happening on a yearly basis compared to the premium they charge in a particular year. They are also interested in monitoring the claim ratio of the vehicles involved in order to offer competitive premium charges to future customers. The sample cited depicts the number of accidents and claim ratio in a bar chart format with plotting lines depicting the other two variables. Such a model suffices in easy comprehension of the data over the years which in some way also helps analyze performance of the company.

The above mentioned depictions and explanations are brief overviews on types of visuals and presentations desired by different kinds of organizations and stakeholders. They are not exhaustive and highlight the features that are relevant to the samples that have been cited along with them.

References:

1.    https://sense-demo.qlik.com/sense/app/2c4b45b7-bb43-46b7-83fb-b5cbe14cff18/sheet/85d464df-836c-4324-bbfb-becc6eacb5dd/state/analysis

2.    http://www.datawatch.com/solutions/industries/financial-services/

3.    https://www.google.com/search?q=data+visuals+for+insurance+industry&espv=2&biw=1422&bih=691&tbm=isch&tbo=u&source=univ&sa=X&ved=0ahUKEwj8jLmYgefLAhUT52MKHU0SB-wQsAQIOw#tbm=isch&q=data+visuals+for+automobile+insurance&imgrc=aOhM7UGzKHBMxM%3A

Structured & Unstructured Data/ Data Warehouse

Structured & Unstructured Data/ DW

Recent advances in the field of business intelligence and analytics definitely calls for the research and analysis of the highly complex domain of unstructured data as compared to the conventional forms of structured data. To start the discussion, it will be great to define these two disparate yet integrated domains of data for the purpose of laying down the foundation for this discussion.

Structured data:

The most common form of data that is organized in the form of tables (Rows and Columns), has well-defined data types (Alpha, numeric, alphanumeric) without any ambiguities in precision. This form of data is quite easy to be loaded into analytic tools without or with very little pre-processing. The analysis with such a kind of data is quite easy in terms of effort required. The data here is ready to be understood by the computer/machine.

Irony here is that though this has been used over decades, yet it consists of only about 20% of data that is available to develop valuable business insights.

Unstructured Data:

This form of data can be considered as nearly opposite to what structured data is. It consists of unorganized data that is void of well- defined data or data types ready to be comprehended by machine/ computers. About 80% of valuable data is composed of this type. The most common examples can be emails, word documents, PDF, social media data etc. A lot of effort is required to be put in terms of extract, transform, and load to make this data ready for analysis. It can be considered as data that is readily human comprehensible but not machine ready. Recent developments in technology have opened up this unexplored domain for valuable analysis.

Summary:

·      Present state of data and data warehousing for analysis

The ‘industrial revolution of data’ as they coin it, is what the organizations are witnessing globally today. Moreover, the proactive ones have already started putting their resources to work in an effort to gain the maximum out of it. The Big Data includes all forms of data ranging from unstructured, semi-structured to structured data. The development of tools and techniques is in parallel with the unveiling of the exabytes of data, storage techniques are facing a challenge to cope up with the times. As an example, the famous retail giant Walmart handles more than one million customer transactions on hourly basis that are fed into the databases storing more than 2.5 petabytes. Other technology endeavors such as IoT, Cloud implementation, security enhancements are complimenting this abundance of data today. Yet, as per the industry experts, this is just the beginning of an era that is yet to witness full- blown caliber of analytics to assist in decision- making for organizations.

Without any doubts, Data Warehousing is the perfect answer to the analysis of the kind of data available today. The fundamentals of the concept still remain unaltered, however development and research is a pervasive process to leverage the powerful abilities of DW with other technology advancements. There are tools available today that can harness data from even unstructured data based on a set of rules/ logic and fed into the DW. A key example for today’s state of DW can be witnessed from the way large retail giants like Amazon are able to offer customized shopping recommendations from the browsing pattern of their customers. The elements of dynamism and real- time data are the gold coins, DW has the ability to dig them out in an efficient manner.

·      Limitations of DW for different data types:

DW might have evolved to a great extent as far as the structured data is concerned but it still has limitations when it comes to unstructured data- one with more potential. One of the key concerns for analysis of unstructured data from a DW is the cost/ benefit ratio given the enormous resources and effort required to pre-process data to make it DW ready. Not only the processing, but the ballooning effect in the databases on account of massive amounts of data to be handled is another concern. Managers are unaware of the results to expect after putting in the massive amount of effort in handling these kinds of data and whether or not this will enable the profit drive for their organizations. The latency on account of pre-processing of data might hinder the ultimate objective of real time analytics. A lot needs to be done to leverage the data warehousing capabilities with the raw data available to gain full- blown advantages and this is a pattern witnessed in almost all the technologies built so far.

·      Future Trends in DW:

(1)    More Capable Data Warehouses

The burgeoning of data volume and types calls for fine-tuned DW’s that are versatile enough to handle these advancements. With memory requirements increasing on the warehouses, cost factor would need to be watched out for. A proportionate increase in cost of new system with increase in memory requirements does not justify well. Capability also needs to be enhanced in the areas of over-the-cloud deployment, mobile technology enabled systems that have a location independent appeal to the most avid viewers who are the managers of organizations to get a sneak- peek whenever, wherever required.

(2)    Physical and logical consolidation for cost control

As discussed above, to control the ballooning effect in cost, systems would require more of logical than of physical enhancements. The logical enhancements might come in the form of virtual systems, tightly synced databases that have the ability to reflect the real-time situations without any significant latencies in order to gain the most out of the data warehouses. A significant effort needs to be put in this regard.

(3)    Real- time analytics

The huge warehouses need to have the caliber to give insights to real-time updates from the system. Time has always been an invaluable resource to any organization, given the shrinking nature of window period for accommodating latencies in analysis and prediction today, efforts towards reduction of processing times from the system would need to be pushed in an integrated manner.

(4)    Cloud Integration

The developments in the cloud technology and its allied advantages call for a complimenting effect from the data warehousing front as well. Given the advantages of cost reduction and mobile access to data from anywhere, next step for DW’s is definitely directed in this direction. Moreover, the fundamentals of data warehousing have the most significant advantages aligned from this horizon. Leveraging cloud with DW is both a win-win situation and the demand of the times.

(5)    Beyond dashboards and reports- integrating day-to-day activities

The dashboard and reporting abilities from the DW that are instrumental in strategy formulation for driving the organization towards achievement of business objectives are far too limited to harness the full potential of the DW’s. Real advantages lie in the promising value it has in guiding everyday work of the organizations today. Things as small as budgeting of cafeterias in the organization would be tackled both qualitatively and quantitatively if the data is fed in appropriate manner.

References:

·       https://www.youtube.com/watch?v=WBU7sW1jy2o

·       http://www.sherpasoftware.com/blog/structured-and-unstructured-data-what-is-it/

·       https://www.google.com/search?q=structured+and+unstructured+data&espv=2&biw=1280&bih=622&source=lnms&tbm=isch&sa=X&ved=0ahUKEwiYl9KB3JnLAhUX02MKHYlhCHgQ_AUIBygC#imgrc=FGbxX11Hi0QyOM%3A

·       http://www.economist.com/node/15557443

·       http://www.forbes.com/sites/oracle/2014/03/10/the-top-10-trends-in-data-warehousing/#754c87391123

Popular CEO Metrics and Dimensional Models

Popular CEO Metrics and Dimensional Models

Company Name: Asian Paints Limited, India

Introduction:

Asian Paints Limited is a chemical manufacturing and distributing company headquartered in Mumbai, India. It’s an established company with a huge market cap focused on the areas of decorative and industrial paints, varnishes, primers and allied products. Whereas it carries its operations in India under this name, international operations are carried under its subsidiary Berger International Limited that operates in South-East Asia, gulf and some other regions. For its operations, the company relies on its manufacturing units in India and the vast network of dealer channels to which it supplies both directly and indirectly via authorized distributors. Its establishment dates back to 1942. The company trades on the Bombay Stock Exchange and has shown good financial figures over the decade. It has been showing steady growth in the stock market over the years. The company is led by Mr. KBS Anand who is the CEO and MD of the company. For the analysis of the performance of the company by higher management, figures like those on balance/income sheets might seem important, yet these are just the scorecard of the operational metrics that help derive these figures.

From the perspective of monitoring performance, following metrics would seem vital for analysis by the CEO of the company:

1.      On-time Delivery

This metric focuses on the customer attentiveness aspect of the company operations. It is targeted towards monitoring of the system that ensures delivery of the order received on a timely basis. Irregular delivery times have a tremendous potential to hamper the performance of the company as this would attract higher rates of customer attrition, which brings down the performance.

2.      Manufacturing Cycle times

This metric measures the time it takes for manufacturing unit to turn the raw material into finished goods since the time order is released to it. This would help in determining the strategies to offset the over stocking or lag in delivery times.

3.      Yield

This metric measures the percentage of products manufactured correctly the first time as desired in the contract. Any decisions regarding quality of production, machinery revision could be based on these.

4.      Customer returns

This metric helps in analyzing the product satisfaction amongst the customers. If this is high, it is highly likely the customer may move to some other company which is never desirable for the company.  

5.      Throughput

Measurement of the quantity of production per manufacturing unit or division. This helps in decisions about setting up of more units to fulfill the demand of the consumers.

6.      Utilization of capacity

This is the measurement of percentage of full capacity of the manufacturing units that is actually realized. It helps understand the shortcomings, if any in the production that would further help in the rectification of production problems.

7.      Inventory/ Turns

This is the measurement of amount of inventory required to garner a given cost of goods sold by the company. Higher management is always interested in maintaining an equilibrium between inventories stored and required so that cash is not over struck.

8.      Safety incidents

This is the measurement of accidental cases reported in the company. It is directed towards employee satisfaction. Had there been more incidents, the management needs to decide upon investment in safety mechanisms.

9.      Manufacturing cost/ Revenue %

This is a measurement of the total cost of products to the company as a percentage of revenue generated. This is highly visible metric towards higher profit margins and efforts need to be put in to ensure this ratio to be at minimum while maintaining the quality.

10.  Net Profit

The management needs to be aware of the net profit it has made to make conclusions for its performance over the previous fiscal periods in order to report to stakeholders of the company. It would also need to backtrack the flaws in case there is a decreasing slope observed.

11.  Productivity per Employee

Employee performance is measured by the revenues generated by the company’s operations divided by its strength of employees devoted towards a unit. It helps in formulation of employee policies.

It is worth noticing that this might not be a comprehensive list of all the metrics desired by the manufacturing sector CEO’s or this company in general. However, it lays down a big picture of what CEO’s need to consider while analyzing their company’s performance. These indicators then serve as trigger points in formulation of strategies and policies towards setting up of targets for the following fiscal periods and laying down means of achieving them.

Dimensional Model Analysis:

For the DW/BI capabilities into the analysis of company performance, each type of dimensional model (transactional, periodic snapshot and accumulating) would be used by various supply chain points. Retailers would lay interest on transactional and periodic snapshots while higher management would be more interested in combination of higher grain based models with roll-up capabilities.

The CEO of such a company would be interested in periodic snapshots with granularity of date dimension as monthly, quarterly and yearly for each of the modules of the supply chain individually. They would be able to identify bottlenecks such as lower production in the manufacturing sector or disturbance of equilibrium in inventory management from such a dimensional model. However, to gain knowledge of the complete picture of the company as a whole, an accumulating snapshot would also be required that explains the complete story behind the whole pipeline from purchase of raw material to delivery of the finished products to the customer. This would help emphasize upon the incentive strategies for employees and stakeholders.

Rolled-up visual trends from periodic snapshots would complement the story from the accumulating snapshot that would assist in closing in on the areas that require attention.

Popular Dimensions that would be necessarily needed are as follows:

Date

Customer

Warehouse

Manufacturing facility

Product

Logistics

…and others as per the specific requirements of analysis.

References:

·       https://www.google.com/finance?cid=672768

·       https://en.wikipedia.org/wiki/Asian_Paints

·       http://www.sap.com/customer-testimonials/chemicals/asian-paints.html

·       http://blog.lnsresearch.com/blog/bid/188295/28-Manufacturing-Metrics-that-Actually-Matter-The-Ones-We-Rely-On

BI Tools Comparison

Popular BI/Analytics Tools

The software industry today has a lot of Business Intelligence & Analytics tools available in the market. The number has shifted to hundreds as compared to the late '90's where there were just a bunch of them. The product differentiation and features have added on to the growing popularity of this technology. This has not only helped the big industries in effective decision making but also the small ones. Almost all established industry units with even slightest capabilities in IT aim at utilization of benefits that these tools have to offer.

Whereas a general discussion on the best tools available would attract the factor of popularity, there are certainly many other factors that are certainly important to analyze from the perspective of right choice of the BI suite. One of the prominent ones being that of investment required. Whereas, a fortune 500 company would not have issues with expensive software suites, small and medium sized companies would want capabilities that would just suffice their business needs and not be heavy on the pocket at the same time. Another view that would support this is the technologies that a product would support. All the industries would of course not look at the products from big data analytics view for at least a decade from now.

Based on above cited factors and other prominent ones, the following comparison matrix highlights the rank of the popular BI tools available in market today based on certain important criterion and their importance to industry units globally.

Criteria	Weight	TIBCO	TABLEAU	QLIK	SAP BW	MS Power
Big Data Analytics	30%	9	8	7	5	6
Investment	30%	5	4	2	5	7
Collaboration	20%	8	9	7.5	6	7.9
Mobile BI	10%	7	9.5	8	6.8	7.5
Visual Standards	10%	5.5	9.5	8	7	8
Points	100%	7.05	7.3	5.8	5.58	7.03
Rank		2	1	4	5	3

The popular BI suites are ranked according to the weighted mean of the scores assigned to them on various criterion listed on the left. The descriptive explanation for each one of them is as follows:

1.     Big Data Analytics

The advent of Big Data and its analysis over the years has made its use quite ubiquitous. Today, the use of Big Data is not limited to large size industries. Even the small and medium industries are attracted towards the benefits offered by bigger, diverse data structures that have a fortune of useful information embedded in them. For this, the BI tool in use should have in built features that could handle terabytes of data, process them and put them into useful, easy-to-understand visualizations. For this important fact, it’s been assigned a weight of 30 %. TIBCO ranks the best amongst the tools analyzed due to its state-of-the-art abilities to handle big data. The prominent features of this product include:

Visualizing Data: quick to analyze and view caliber that offers interesting insights

Big Data Connectors: availability of in-memory, in-data source, and on-demand data access methods

Distributed Computing: In addition to query processing, statistical and machine learning algorithms can also be run easily

2.     Investment Per User

This criterion reflects the cost of purchasing a single-user license towards the concerned BI tool. It also reflects the Return-On-Investment (ROI) that the product suite has to offer based on user consensus and public forums. This feature is again a prominent one as it’s one of the major deciding factors for firms on the lookout for a BI suite. The extent of this factor magnifies when its cost is calculated organization wide wherein licenses need to be procured for hundreds or thousands of employees.

On this factor, QLIK is the most expensive whereas MS Power BI is the cheapest. (Low scores indicate that the tool is expensive as compared to ones with a higher score).

However, the exact cost could not be calculated or known without direct contact with the officials. Other products, such as TABLEAU, SAP and TIBCO rank moderately in this criterion.

3.     Collaboration Ability

Just like other technologies and their sharing abilities, today’s business user demands sharing ability on BI Platform as well. This feature comes into large-scale use especially in large organizations where there are large number of people involved in the decision making process. The collaborating and version feature on BI platforms ranks quite high in terms of criterion to choose a product.

On this criterion the product from TABLEAU ranks the highest. This suite includes sharing ability as well as the power to get the analyzed data and visualizations online. These may then be made public to be viewed by all or hidden from general public viewing.

Moreover, the collaborating feature by posting comments to be viewed by another user from the organization is access permission based thus ensuring security. SAP ranks the lowest on this one.

4.     Mobile BI

Mobile BI can be elaborated as the ability of mobile workforce to peep into business insights through use of BI and analytics on applications over mobile devices. This is not only an inherent advantage but also a kind of necessity these days. With the advancement of technology and more reliance on the use of mobile devices and applications, it’s almost inevitable that everyone around demands this functionality in the near future.

Tableau ranks at the top on this criterion. It has all the abilities that one could think of with respect to mobile technology. The drag-and-drop feature for almost every feature analysis makes this product in sync with the mobile requirements. Other products in comparison do have some capability but are definitely not at par with the offerings from Tableau.

5.     Visual Standards

This feature is a self- explanatory one that refers to the presentation standards of the BI product. Though one would think that this might be the decorative or finishing front after the in-depth analysis, yet the appeal to human mind comes only when they see what it is all about. The senior management in almost all the organizations today don’t want to exercise their brains and put in time understanding the charts and graphs provided to them. What is expected is that they should be able to make out and distinguish the trends and patterns just by a glimpse at it. Though this demands a great designing skill set on the analyst part. Here again, Tableau ranks the highest from our list of products being compared here. It has got nearly all the kinds of charts, graphs, maps etc. that can be manipulated by just a small strike-through by a mouse. Other products like Qlik also have competitive visuals and can be thought of as emerging leaders on this front.

References: www.youtube.com

                     www.google.com

                     http://www.softwareadvice.com/bi/

                    http://www.butleranalytics.com/enterprise-bi-platforms-compared/

                     http://go.sap.com/product/analytics/

                    https://community.tableau.com/