Survey Results: Challenges and Opportunities for Professional Services Firms
Earlier this summer one of my clients, Deltek, sponsored IDC in a survey and white paper on current challenges and…
New Technology Is Not an Easy Button for Big Data
It is good to remember in today’s hype-filled big data world that there is no “easy” button for big data.…
Importance of Social Media Analytics
We can safely say that the decline in outbound marketing is due to a fundamental shift in consumer behaviour. People…
Are Business Intelligence dashboards on the brink of extinction?
Why most people get them wrong Why most people get them wrong Like the familiar hum of a loved one’s car…
5 Rules for Better Sales Analytics
Sales performance isn’t just about sales numbers and sales activities. Sure, trends in sales success are deeply tied to sales…
Interactive Analytics and OLAP – Part III
In the part II of interactive analytics and OLAP, we left a question: Can the narrowed OLAP be used to complete the computation process as follows (marketing and sales data analysis)? In the part II of interactive analytics and OLAP, we left a question: Can the narrowed OLAP be used to complete the computation process as follows (marketing and sales data analysis)?The first in customers whose purchases from the company account for half of the sales volume of the company of the current year;The stocks which go up to the limit for three consecutive days within one month;Commodities in the supermarket which are sold out at 5 P.M for three times within one month;Commodities whose sales volumes in this month have decreased by more than 20% over those of the preceding month; Of course NOT!Currently OLAP system has two key disadvantages:The multi-dimensional cube is prepared in advance by the application system and user does not have the capability to temporarily design or reconstruct the cube, so once there is new analysis demand, it is necessary to re-create the analytics cube.The analysis actions could be implemented by cube are rather monotonous. The defined actions are quite few, such as the drilling, aggregating, slicing, and pivoting. The complicated analysis behavior requiring multi-steps is hard to implement.Although the current OLAP tools are splendid regarding its look and feel, few on-line analysis capabilities powerful enough are provided actually.Then, what kind of OLAP do we need? What kind of OLAP tools we need? It is very simple, and we need a kind of on-line analytical system that can support evaluation process, which SQL data computing or excel computation can handle.Technically speaking, steps for evaluation process can be regarded as computation regarding data (query can be understood to be filter computation). This kind of computation can be freely defined by user and user can occasionally decide the next computation action according to the existing intermediate result, without having to model beforehand. Additionally, as data source is generally database system, it is necessary to require this kind of computation to be able to very well support mass structured data (tools like esProc) instead of simple numeric computation. And evaluation process is what business need especially in marketing and sales data analysis.Then, can SQL (or MDX) play this role? SQL is indeed invented for this aim and it owns complete computation capability and it adopts a writing style similar to natural language.But, as SQL computation system is too basic, it is very difficult and over-elaborate to achieve complex computation by a SQL data computing, such as problems listed in the preceding paragraphs. It is even not so easy for programmers who have received professional training, so ordinary users can only use SQL to implement some of the simplest queries and aggregate computation (based on the filter and summarization of a single table). This result leads to the fact that the application of SQL has already deviated far away from its original intention of invention, almost becoming the expertise for programmers.We should follow the working thought of SQL to carefully study the specific disadvantage of SQL and find the way to overcome it in an effort to develop a new generation of computation system, thereby implementing the evaluation process, namely, the real OLAP, instant data analytics.Related Articles:Interactive Analytics and OLAP - Part IIInteractive Analytics and OLAP - Part I
Why Business Needs Public Data
With over 30 years in retail site location strategy, I used Census data every day to analyze business critical issues.…
Interactive Analytics and OLAP – Part II
After the first stage of real application process of the OLAP in interactive analytics and OLAP - Part I, we will start OLAP application of stage 2. After the first stage of real application process of the OLAP in interactive analytics and OLAP - Part I, we will start OLAP application of stage 2. Those guesses in part I of interactive data analytics are just the basis for forecast. After operating for a period of time, a constructed business system can also accumulate large quantities of data (so called complex data calculation), and these guesses have most probably been evaluated by these accumulated data, when evaluated to be true, they can be used in forecast; when evaluated to be false they will be re-guessed. It needs to be noted that these guesses are made by users themselves instead of the computer system! Instant data analytics is started by human being in OLAP. What a computer should do is to help a user to evaluate according to the existing data, the guess to be true or false, namely, on-line data query (including certain aggregation computation). This is just the application process of OLAP. The reason why on-line analysis is needed is that many query computations are temporarily required after a user has seen a certain intermediate result. In the whole process, model in advance is impossible and unnecessary (Raqsoft esProc is born to deal with these issues).We call the above process evaluation process, whose purpose is to find from historical data some laws or evidences for conclusions, and the means adopted is to conduct interactive query computation on historical data. And this process can be a complex data calculation. The following are a few examples actually requiring computations (or queries): The first n customers whose purchases from the company account for half of the sales volume of the company of the current year; The stocks which go up to the limit for three consecutive days within one month; Commodities in the supermarket which are sold out at 5 P.M for three times within one month; Commodities whose sales volumes in this month have decreased by more than 20% over those of the preceding month; …Evidently, this type of computation demand is ubiquitous in business analysis process and all can be computed out from historical database.Then, can the narrowed OLAP be used to complete the above-mentioned data computation process?In the third part of Interactive Analytics and OLAP, i will answer the question above.Sponsored by http://www.raqsoft.comTo be continued... Related Articles:Interactive Analytics and OLAP - Part I
Open Data: What’s It Hiding?
Let’s get this straight – I’m all for open data. Yet the fanfare about open data is taking up so…
Building Your Analytical Team: Tips for Executives
As the concept of using analytics as a strategic advantage is gaining more and more traction, many organizations are asking…
