Cookies help us display personalized product recommendations and ensure you have great shopping experience.

By using this site, you agree to the Privacy Policy and Terms of Use.
Accept
SmartData CollectiveSmartData Collective
  • Analytics
    AnalyticsShow More
    data mining to find the right poly bag makers
    Using Data Analytics to Choose the Best Poly Mailer Bags
    12 Min Read
    data analytics for pharmacy trends
    How Data Analytics Is Tracking Trends in the Pharmacy Industry
    5 Min Read
    car expense data analytics
    Data Analytics for Smarter Vehicle Expense Management
    10 Min Read
    image fx (60)
    Data Analytics Driving the Modern E-commerce Warehouse
    13 Min Read
    big data analytics in transporation
    Turning Data Into Decisions: How Analytics Improves Transportation Strategy
    3 Min Read
  • Big Data
  • BI
  • Exclusive
  • IT
  • Marketing
  • Software
Search
© 2008-25 SmartData Collective. All Rights Reserved.
Reading: Interactive Analysis and Relate Tools – Part I
Share
Notification
Font ResizerAa
SmartData CollectiveSmartData Collective
Font ResizerAa
Search
  • About
  • Help
  • Privacy
Follow US
© 2008-23 SmartData Collective. All Rights Reserved.
SmartData Collective > Analytics > Interactive Analysis and Relate Tools – Part I
AnalyticsBusiness Intelligence

Interactive Analysis and Relate Tools – Part I

raqsoft
raqsoft
6 Min Read
SHARE

I will talk something about interactive analytics and a series of related tools in business intelligence industry. For those who doesn’t know much about interactive analysis and its tools, I wish my articles are helpful to you. I will explain interactive analytics from its definition and with case examples to help you understand today.

I will talk something about interactive analytics and a series of related tools in business intelligence industry. For those who doesn’t know much about interactive analysis and its tools, I wish my articles are helpful to you. I will explain interactive analytics from its definition and with case examples to help you understand today.

What’s the definition of interactive analysis? Interactive analysis is a cycle analysis procedure of assumption, validation, and adjustment to achieve the fuzzy computation goal.

The interactive analysis is the real on-line analysis to solve the complex computation problem in the real world, and it is one of the key points in the business computation.

More Read

House Hearing on Hedge Funds
SOA for Process and Data Integration
Situational Intelligence: Not Just Another Fancy Term
Are You Transformed? The MIT Report on Analytics
24 Questions to Ask when Preparing Data for Analysis

Let us explain the interactive analysis with a common example in the business activities. Mainly, a interactive data analytics can be divided into 6 steps.

Step 1 Set the goal

Why the sales volume this month greatly exceeds that of the previous month?

Obviously, this is a fuzzy computation goal with several possible answers. You cannot get the result directly using any analysis mode.

Step 2 Guess the possible branch

Since there are several possibilities to give rise to the sales volume increase, the analyzer has to check every possibility, such as:

l         Orders numbers increase

l         Appearance of large orders

l         Intensive consumption of specific customer base, for example the intensive screening the movies of children in the summer holiday

l         Improvement of process

l         Launching a marketing campaign

……

Obviously, a certain level of business knowledge is required to make these assumptions and the keen sense of smell to the circumstances inside and outside the enterprise. This is a relatively personalized effort.

Step 3 Branch validation

Based on the possibility and characteristics of data, the analyzer will choose a branch to start the analysis, such as Increase of Orders. If the number of orders does not increase through the calculating for validation, then it indicates that this assumption is not correct. You need to validate the next assumption to carry on the cyclic analysis.

For example, by going through the validation on this branch of Appearance of Large Order, the analyzer finds this is correct, and thus this branch can be justified.

Step 4 In-depth exploration and mining

These possibilities are usually the apparent cause instead of the root cause. To really settle the problem, you will have to drill down step by step to reach the core. For example, the appearance of large order may result from:

l         The new salesmen is highly capable

l         The new sales policy of the company boosts the large order

l         Intensive procurement of clients from a certain sector

……

It is obvious that the process of drill-down is a cyclic procedure. The analyzer must judge on the characteristics of data at that specific point to choose the branch of the highest possibility, so as to progress level by level, until the problem is solved.

Step 5 Solve problem

The procedure of exploration and mining does not require the unlimited drilling down. The whole procedure can put an end once a clear answer enough to make a decision is found. For example, through the validation, the Centralized Procurement in a Certain Sector is determined just the root cause. Then, this is enough for analyzer to make a decision: The sales volume can keep rising by simply beefing up the sales forces and efforts in this sector since the recent sales rise is the result of centralized procurement by the clients in this sector.

Step 6  More computations

To this step, the computation goal is achieved. However, we can realize more business values through more computation on the basis of the existing results, such as:

l         Find the list of customers in this sector

l         Find the list of salesman which are good at this sector

l         Find the reason why the client in this sector increase the procurement quantities abruptly

l         Find the abnormal actions in the sector related to this sector and the downstream/upstream sector.

And this is the end of first part for Interactive Analysis and Related Tools, and i am sure from above, you have a basic understanding about interactive analysis.

Thanks everyone for reading and providing comments. If you have any questions, please let me know. Your feedback is valued and appreciated!

To be continued…sponsored by datakeyword.

 

Share This Article
Facebook Pinterest LinkedIn
Share

Follow us on Facebook

Latest News

data mining to find the right poly bag makers
Using Data Analytics to Choose the Best Poly Mailer Bags
Analytics Big Data Exclusive
data science importance of flexibility
Why Flexibility Defines the Future of Data Science
Big Data Exclusive
payment methods
How Data Analytics Is Transforming eCommerce Payments
Business Intelligence
cybersecurity essentials
Cybersecurity Essentials For Customer-Facing Platforms
Exclusive Infographic IT Security

Stay Connected

1.2kFollowersLike
33.7kFollowersFollow
222FollowersPin

You Might also Like

Future Trends in Business Rules (with a little help from my friends)

7 Min Read

Fascination with Hadoop pushes, pulls Big Data analytics into mainstream. (Part One)

6 Min Read
Image
AnalyticsBig DataIT

IoT hits pay dirt where needs and capabilities align

7 Min Read

The Slow Demise of 4GLs

5 Min Read

SmartData Collective is one of the largest & trusted community covering technical content about Big Data, BI, Cloud, Analytics, Artificial Intelligence, IoT & more.

AI and chatbots
Chatbots and SEO: How Can Chatbots Improve Your SEO Ranking?
Artificial Intelligence Chatbots Exclusive
data-driven web design
5 Great Tips for Using Data Analytics for Website UX
Big Data

Quick Link

  • About
  • Contact
  • Privacy
Follow US
© 2008-25 SmartData Collective. All Rights Reserved.
Go to mobile version
Welcome Back!

Sign in to your account

Username or Email Address
Password

Lost your password?