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Drill Down Using Slicer in Power BI

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3 min

  This article shows how to drill down to the next level in a visualization within Microsoft Power BI, when a filter is applied using a Slicer. This could be applied even if multiple selections are made within the slicer. For example – We have a car with 3 model series (L Series, S Series, T Series) in the slicer. Business requires to show the values at this level when no selection is made. Once ...

 

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This article shows how to drill down to the next level in a visualization within Microsoft Power BI, when a filter is applied using a Slicer.

This could be applied even if multiple selections are made within the slicer.

For example – We have a car with 3 model series (L Series, S Series, T Series) in the slicer. Business requires to show the values at this level when no selection is made. Once a selection is made, the visual should show the next level in the hierarchy.

Visualisation without any filter selected (Shows the top level):

 

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Visualisation with single filter selected (Shows the next level for S Series):

 

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Visualisation with multiple filters selected (Shows the next level for S Series & T Series):


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In order to achieve the above in Power BI, follow the below steps,

Step 1: We have a ‘Model’ hierarchy setup in Power BI. The hierarchy was imported in Power BI using Octane connector from TM1. You could use any other hierarchy setup.

Lowest level is ‘model’ column with unique model names.


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Step 2: Create another table ‘Model Levels’ (used in this example) with 3columns from the above hierarchy in Step 1; 1st column being the Parent 2 & Parent 1; 2nd Column if Parent 2 then value is ‘L2’ else ‘L1’; and 3rd column being the lowest level model names.

 

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Step 3: Create a relationship between ‘Model’ & ‘Model Levels’ table with the lowest level model names. Select ‘Both’ in Cross Filter direction while creating the relationship.


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Step 4: Create a measure ‘Sales_By_Model’ which calculates the actual value depending on the filter selections. If its selected, It drills to the next level, else, shows the top level.

 

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Step 5: Create a slicer using Parent 2 from the original ‘Model’ hierarchy table.

 

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Step 6: Create a visualisation using the Model Name (Parent 2 or Parent 1) from ‘Model Levels’ table created in Step 2 and for values, use the measure created in Step 4.

 

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Planning Analytics Audit log – Little known pitfall

0
2 min

The blogs brief about the challenge faced post enabling the Audit log in one of our client's environment. Once the audit log was turned on to capture the metadata changes, the Data Directory backup scheduled process started to fail.

After some investigation, I found the cause was the temp file (i.e., tm1rawstore.<TimeStamp> ) generated by the audit log by default and placed in the data directory.

The Temp file is used by audit log to record the events before moving it to a permanent file (i.e., tm1auditstore<TimeStamp>). Sometimes, you may even notice dimension related files (i.e., DimensionName.dim.<Timestamp>), and these files are generated by audit log to capture the dimension related changes.

The RawStoreDirectory is a tm1.cfg parameter related to the audit log, which helped us resolve the issue. This parameter is used to define the folder path for temporary, unprocessed log files specific to the audit log, i.e., tm1rawstore.<TimeStamp>, DimensionName.dim.<Timestamp>. If this Config is not set, then by default, these files get placed in Data Directory.

RawStoreDirectory = <Folderpath>

 

Now, let's also see other config parameters related to the audit logs

 

AuditLogMaxFileSize:

The config parameter can be used to control the maximum size audit log file to be before the file gets saved and a new file is created. The unit needs to be appended at the end of the value defined ( KB, MB, GB), and Minimum is 1KB and Maximum is 2GB; if this is not specified in the TM1 Cfg then the default value would be 100 MB.

AuditLogMaxFileSize=100 MB

 

AuditLogMaxQueryMemory:

The config parameter can be used to control maximum memory the TM1 server can use for running audit log query and retrieving the set. The unit needs to be appended at the end of the value defined ( KB, MB, GB) and Minimum is 1KB and Maximum is 2GB; if this is not specified in the TM1 Cfg then the default value would be 100 MB.

AuditLogMaxQueryMemory=200 MB


AuditLogUpdateInterval:

The config parameter can be used to control the amount of time the TM1 server needs to wait before moving the contents from temporary files to a final audit log file. The value is taken in minutes; that is, say 100 is entered, then it is taken has 100 minutes.

AuditLogUpdateInterval=100

 

That's it folks, hope you had learnt something new from this blog.

Planning Analytics Administration: An Alert (Proactive Mechanism)

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3 min

Gone days, where we had no control/alerts mechanism on the TM1 database, CPU/memory it consumes, react at the nick of the moment before TM1 server crashes.

I am sure all TM1 lovers, administrators and business users who had these experiences in the past would connect to what I am referring to. For all new Planning Analytics users, in earlier versions of TM1/ Planning Analytics, we had little overview on how much on RAM/ memory can a TM1 instance use/utilize or have an inbuilt alter mechanism. 

TM1 Database Alert Mechanism: 

Issue: 

Most of you know, TM1 Server loves memory/RAM, more memory available the better performance/processing you get. Due to the trade-off between the cost and memory availability, there has always been a cap on upper limit on RAM available to TM1 Server.   

What is new: 

We now have an inbuilt mechanism in Planning Analytics Workspace, wherein we can set certain configuration and look for alters at a different level.  

Administrators can now set, database threshold and alter configurations in a single tab on the Database settings page for the individual database in Planning Analytics Administration. 

Isn’t that the good news! To use this, Planning Analytics Workspace version must be 2.0.46 or higher. In the previous version of Planning Analytics Administration, it was not possible to apply unique settings for each database, thresholds and alerts were set on separate tabs of a configuration page, but settings were applied to all databases in the environment. 

Navigation

For database settings, go to the Administration page, click Database as shown below. 

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Click on settings (highlighted), Database Setting, move to Thresholds and alerts. 

The administrator can enter values for Warning threshold and Critical threshold and enable alert as different resource usages, as shown below. 

 

Screen Shot 2020-03-04 at 11.28.24 am

 

The administrator can also set thread auto-refresh time interval. 

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Wonderful, now you can implement these in your environment, any doubts – we are here to help you for sure! Contact us today to find out how we can help you leverage your data for true business intelligence. 

 

 

You may also like reading “ Predictive & Prescriptive-Analytics ” , “ Business-intelligence vs Business-Analytics ” ,“ What is IBM Planning Analytics Local ” , “IBM TM1 10.2 vs IBM Planning Analytics”, “Little known TM1 Feature - Ad hoc Consolidations”, “IBM PA Workspace Installation & Benefits for Windows 2016”. 

 

Octane Software Solutions Pty Ltd is an IBM Registered Business Partner specialising in Corporate Performance Management and Business Intelligence. We provide our clients with advice on best practices and help scale up applications to optimise their return on investment. Our key services include Consulting, Delivery, Support and Training. Octane has its head office in Sydney, Australia as well as offices in Canberra, Bangalore, Gurgaon, Mumbai, and Hyderabad. 

 

Planning Analytics for Excel: Trace TI status

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2 min

IBM has been recommending its users to move to Planning Analytics for Excel (PAX) from TM1 Perspective and/or from TM1 Web. This blog is dedicated to clients who have either recently adopted PAX or contemplating too and sharing steps on how to trace/watch TI process status while running process using Planning Analytics for Excel.

Steps below should be followed to run processes and to check TI process status.

1. Once you connect to Planning Analytics for Excel, you will be able to see cubes on the right-hand side, else you may need to click on Task Pane.

 
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2. Click on the middle icon as shown below and click on Show process. This will help show all process (to which respective user has access to) in Task Pane.

 
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3. You will now be able to see Process.

 

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4. To check/ trace status of the process (when triggered via Planning analytics for excel) right-Click on Processes and click Active processes.

 

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5. A new box will pop-up as shown below.

 
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6. You can now run process from Task pane and check if you can track status in new box popped up in step 5.

 

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7. You can now see the status of process in this box, below is a screen print that shows the for-process cub.price.load.data, process completed 4 tasks out of 5 tasks.

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8. Below screen prints tells us if the status of TI process, they are Working , Completed and Process completed with Errors.

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Once done, your should be able to to trace TI status in Planning Analytics for Excel. Happy Transitioning.

As I pen down my last Blog for 2019, wishing you and your dear ones a prosperous and healthy 2020.

Until next time....keep planning & executing.

 

IBM Planning Analytics Secure Gateway Client: Steps to Set-Up

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6 min

This blog broaches all steps on how to install IBM Secure Gateway Client.

IBM Secure Gateway Client installation is one of the crucial steps towards setting up secure gateway connection between Planning Analytics Workspace (On-Cloud) and RDBMS (relational database) on-premise or on-cloud.

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What is IBM Secure Gateway :

IBM Secure Gateway for IBM Cloud service provides a quick, easy, and secure solution establishing a link between Planning Analytics on cloud and a data source. Data source can reside on an “on-premise” network or on “cloud”. Data sources like RDBMS, for example IBM DB2, Oracle database, SQL server, Teradata etc.

Secure and Persistent Connection :

A Secure Gateway, useful in importing data into TM1 and drill through capability, must be created using TurboIntegrator to access RDBMS data sources on-premise.

By deploying the light-weight and natively installed Secure Gateway Client, a secure, persistent and seamless connection can be established between your on-premises data environment and cloud.

The Process:

This is two-step process,

  1. Create Data source connection in Planning Analytics Workspace.
  2. Download and Install IBM Secure Gateway

To download IBM Secure Gateway Client.

  1. Login to Workspace ( On-Cloud)
  2. Navigate to Administrator -> Secure Gate

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Click on icon as shown below, this will prompt a pop up. One needs to select operating system and follow steps to install the client.
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Once you click, a new pop-up with come up where you are required to select the operating system where you want to install this client.

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Choose the appropriate option and click download.

If the download is defaulted to download folders you will find the software in Download folder like below.

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Installation IBM Secure Gateway Client:

To Install this tool, right click and run as administrator.

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Keep the default settings for Destination folder and Language, unless you need to modify.

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Check box below if you want this as Window Service.

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Now this is an important step, we are required to enter Gateway ids and security tokens to establish a secured connection. These needs to be copied over from Secure connection created earlier in Planning Analytics Workspace ( refer 1. Create Data source connection in workspace).

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Figure below illustrates Workspace, shared details on Gateway ID and Security Token, these needs to be copied and pasted in Secure Gateway Client (refer above illustration).

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If user chooses to launch the client with connection to multiple gateways, one needs to take care while providing the configuration values.

  1. The gateway ids need to be separated by spaces.
  2. The security tokens, acl files and log levels should to be delimited by --.
  3. If you don't want to provide any of these three values for a particular gateway, please use 'none'.
  4. If you want Client UI you may choose else select No.

Note: Please ensure that there are no residual white spaces.

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Now click Install, once this installation completes successfully, the IBM Secure Gateway Client is ready for use.

This Connection is now ready, Planning Analytics can now connect to data source residing on-premise or any other cloud infrastructure where IBM Secure Gateway client is installed.

 

You may also like reading “ Predictive & Prescriptive-Analytics ” , “ Business-intelligence vs Business-Analytics ” ,“ What is IBM Planning Analytics Local ” , “IBM TM1 10.2 vs IBM Planning Analytics”, “Little known TM1 Feature - Ad hoc Consolidations”, “IBM PA Workspace Installation & Benefits for Windows 2016”.

What is IBM Watson™ Studio?

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1 min

IBM Watson™ Studio is a platform for businesses to prepare and analyse data as well as build and train AI and machine learning models in a flexible hybrid cloud environment.

IBM Watson™ Studio enables your data scientists, application developers and subject matter experts work together easier and collaborate with the wider business, to deliver faster insights in a governed way.

Watch the below for another brief intro



Available in on the desktop which contains the most popular portions of Watson Studio Cloud to your Microsoft Windows or Apple Mac PC with IBM SPSS® Modeler, notebooks and IBM Data Refinery all within a single instal to bring you comprehensive and scalable data analysis and modelling abilities.

However, for the enterprise, there are also the versions of Watson Studio Local, which is a version of the software to be deployed on-premises inside the firewall, as well as Watson Studio Cloud is part of the IBM Cloud™, a public cloud platform. No matter which version your business may use you can start using Watson Studio Cloud and download a trial of the desktop version today!

Over the next 5 days, we'll ensure to send you use-cases and materials of worth for you to review at your earliest convenience. Be sure to check our social media pages for these.

IBM Planning Analytics (TM1) Vs Anaplan

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4 min

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IBM Planning Analytics (TM1) vs Anaplan

There has been a lot of chatter lately around IBM Planning Analytics (powered by TM1) vs Anaplan. Anaplan is a relatively new player in the market and has recently listed on NYSE. Reported Revenue in 2019 of USD 240.6M (interestingly also reported an operating loss of USD 128.3M). Compared to IBM which has a 2018 revenue of USD 79.5 Billion (there is no clear information on how much of this was from the Analytics area) with a net profit of 8.7 b). The size of global Enterprise Performance Management (EPM) is around 3.9 Billion and expected to grow to 6.0Billion by 2022. The size of spreadsheet based processes is a whopping 60 Billion (Source: IDC)

Anaplan has been borne out of the old Adaytum Planning application that was acquired by Cognos and Cognos was acquired by IBM in 2007. Anaplan also spent 176M on Sales and Marketing so most people in the industry would have heard of it or come across some form of its marketing. (Source: Anaplan.com)

I’ve decided to have a closer look at some of the crucial features and functionalities and assess how it really stacks up.

Scalability 

There are some issues around scaling up the Anaplan cubes where large datasets are under consideration (8 billion cell limit? While this sounds big, most of our clients reach this scale fairly quickly with medium complexity). With IBM Planning Analytics (TM1) there is no need to break up a cube into smaller cubes to meet data limits. Also, there is no demand to combine dimensions to a single dimension. Cubes are generally developed with business requirements in mind and not system limitations. Thereby offering superior degrees of freedom to business analyst.

For example, if enterprise wide reporting was the requirement, then the cubes may be need to be broken via a logical dimension like region of divisions. This in turn would make consolidated reporting laborious, making data slicing and dicing difficult, almost impossible.

 

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Excel Interface & Integration

Love it or hate it – Excel is the tool of choice for most analyst and finance professionals. I reckon it is unwise to offer a BI tool in today’s world without a proper excel integration.  I find Planning Analytics (TM1) users love the ability to use excel interface to slice and dice, drill up and down hierarchies and drill to data source. The ability to create interactive excel reports with ability to have cell by cell control of data and formatting is a sure-shot deal clincher.

On the other hand, on exploration realized Anaplan offers very limited Excel support.

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 Analysis & Reporting

In today’s world users have come to expect drag and drop analysis. Ability to drill down, build and analyze alternate view of the hierarchy etc “real-time”. However, if each of this query requires data to be moved around cubes and/or requires building separate cubes then it’s counterproductive. This would also increase the maintenance and data storage overheads. You also lose sight of single source of truth as your start developing multiple cubes with same data just stored in different form. This is the case with Anaplan due to the software’s intrinsic limitations.

Anaplan also requires users to invest on separate reporting layer as it lacks native reporting, dashboards and data visualizations.

This in turn results in,

  1. Increase Cost
  2. Increase Risk
  3. Increase Complexity
  4. Limited planning due to data limitations

IBM Planning Analytics, on the contrary offers out of the box ability to view & analyze all your product attributes and the ability to slice and dice via any of the attributes. 

It also comes with a rich reporting, dashboard and data visualization layer called Workspace. Planning Analytics Workspace delivers a self-service web authoring to all users. Through the Planning Analytics Workspace interface, authors have access to many visual options designed to help improve financial input templates and reports. Planning Analytics Workspace benefits include:

  1. Free-form canvas dashboard design
  2. Data entry and analysis efficiency and convenience features
  3. Capability to combine cube views, web sheets, text, images, videos, and charts
  4. Synchronised navigation for guiding consumers through an analytical story
  5. Browser and mobile operation
  6. Capability to export to PowerPoint or PDF

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Source : Planning Analytics (TM1) cube

Planning Analytics - Cloud Or On-Premise

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4 min

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This Blog details IBM Planning Analytics On-Cloud and On-Premise deployment options. It focusses & highlights key points which should help you make the decision; “whether to adopt Cloud Or stay on Premise”

 

IBM Planning Analytics:

As part of their continuous endeavour to improve application interface and better customer experience, IBM rebranded TM1 to Planning Analytics couple of years back which came with many new features and a completely new interface. With this release (PA 2.x version as it has been called), IBM is letting clients choose Planning Analytics as Local SW or as Software as a Service (SaaS) deployed on IBM Softlayer Cloud.

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Planning Analytics on Cloud:

Under this offering, Planning Analytics system operates in a remote hosted environment. Clients who choose Planning Analytics deployed “on-cloud” can reap many benefits aligned to any typical SaaS.

With this subscription, Clients’ need not worry about software Installation, versions, patches, upgrades, fixes, disaster recovery, hardware etc.

They can focus on building business models and enriching data from different source systems and give meaning to the data they have. This by converting data into business critical, meaningful, actionable insights.

Benefits:

While not a laundry list, covers significant benefits.

  • Automatic software updates and management.
  • CAPEX Free; incorporates benefits of leasing.
  • Competitiveness; long term TCO savings.
  • Costs are predictable over time.
  • Disaster recovery; with IBM’s unparalleled global datacentre reach.
  • Does not involve additional hardware costs.
  • Environment friendly; credits towards being carbon neutral.
  • Flexibility; capacity to scale up and down.
  • Increased collaboration.
  • Security; with options of premium server instances.
  • Work from anywhere; there by driving up productivity & efficiencies.

Client must have Internet connection to use SaaS and of course, Internet speed plays major role. In present world Internet connection has become a basic necessity for all organizations.

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Planning Analytics Local (On-Premise):

Planning Analytics local essentially is the traditional way of getting software installed on company’s in-house server and computing infrastructure installed either in their Data Centre or Hosted elsewhere.

In an on-premise environment - Installation, upgrade, and configuration of IBM® Planning Analytics Local software components are on the Organization.

Benefits of On-Premise:

  • Full control.
  • Higher security.
  • Confidential business information remains with in Organization network.
  • Lesser vendor dependency. 
  • Easier customization.
  • Tailored to business needs.
  • Does not require Internet connectivity, unless “anywhere” access is enabled.
  • Organization has more control over implementation process.

As evident on-premise option comes with some cons as well, few are listed below.

  • Higher upfront cost
  • Long implementation period.
  • Hardware maintenance and IT cost.
  • In-house Skills management.
  • Longer application dev cycles.
  • Robust but inflexible.

On-premise software demands constant maintenance and ongoing servicing from the company’s IT department.

Organization on on-premise have full control on the software and on its related infrastructure and can perform internal and external audits as and when needed or recommended by governing/regulatory bodies.

Before making the decision, it is also important to consider many other influencing factors; from necessary security level to the potential for customization, number of Users, modelers, administrators, size of the organization, available budget, long term benefits to the Organization.

While you ponder on this, there are many clients who have adopted a “mid-way” of hybrid environment. Under which basis factors like workload economics, application evaluation & assessment, security and risk profiles, applications are being gradually moved from on-premise to cloud in a phased manned.

 

You may also like reading “ What is IBM Planning Analytics Local ” , “IBM TM1 10.2 vs IBM Planning Analytics”, “Little known TM1 Feature - Ad hoc Consolidations”, “IBM PA Workspace Installation & Benefits for Windows 2016”.

For more Information: To check on your existing Planning Analytics (TM1) entitlements and understand how to upgrade to Planning Analytics Workspace (PAW) reach out to us at info@octanesolutions.com.au for further assistance.

Octane Software Solutions Pty Ltd is an IBM Registered Business Partner specialising in Corporate Performance Management and Business Intelligence. We provide our clients advice on best practices and help scale up applications to optimise their return on investment. Our key services include Consulting, Delivery, Support and Training. Octane has its head office in Sydney, Australia as well as offices in Canberra, Bangalore, Gurgaon, Mumbai, and Hyderabad.

To know more about us visit, OctaneSoftwareSolutions.

Predictive & Prescriptive Analytics: IBM Decision Optimisation

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7 min

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Predictive analytics:

Predictive analytics is the practice of extracting information from existing data sets in order to determine patterns and predict future outcomes and trends. A way to predict the future using data from the past.

Predictive analytics brings together advanced analytics capabilities spanning ad-hoc statistical analysis, predictive modelling, data mining, text analytics, optimization, real-time scoring and machine learning. These are tools that help organizations discover patterns within the data and go beyond knowing what has happened to anticipate what is probable to happen next.

  • Use historical information to determine patterns.
  • Once equipped with these patterns, predictive models are built and are used to forecast possible trends and outcomes.
  • Predictive analytics highlights approaching opportunities and potentials for risk to improve the quality of decision-making around upcoming events.

 

Prescriptive Analytics:

Prescriptive analytics is the use of technology to help businesses make better decisions in handling specific events by factoring in the knowledge of possible constraints, available resources, past performance and current situation.

Prescriptive analytics involves mathematical and computational algorithms and goes beyond predicting future outcomes by also suggesting actions and to benefit from the predictions and showing the implications of each decision option.

Prescriptive analytics seeks to determine the optimized solution or best outcome among different choices depending on current constraints, resources and priorities. Prescriptive analytics uses both descriptive and predictive data to determine a specific action to take.

Prescriptive Analytics capabilities :

Prescriptive Analytics hold below features

  • Prescriptive Modelling
  • Uses mathematical and computations models
  • Optimized solutions
  • Continually take in new data to re-predict and re-prescribe
  • Automatically improve prediction accuracy and prescribing better decision options
  • Visualization
  • Prescriptive analytics incorporates both structured and unstructured data

 

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IBM Decision Optimisation is one such solution from IBM.

 

IBM Decision Optimisation:

IBM Decision Optimisation is a prescriptive analytics solution that enables organisations in commerce, manufacturing, financial services, healthcare, telco, government and other highly data-intensive industries to make better decisions and achieve business goals by solving complex optimisation problems.

IBM Decision Optimisation solves business problems using Mathematical and Constraint programming.

IBM Decision Optimisation solutions provide features that help create optimization models, either using general programming language APIs, like Python, Java or OPL to solve the breadth of optimization models, using proven and powerful optimization engines.

 

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IBM Decision Optimisation is an integral part of IBM Watson® Studio, so users can benefit from all data science features of IBM Watson Studio, like access to machine-learning models, the ability to pass output from predictive analytics to the Decision Optimisation engine, access to open notebook features, visualization features and data connectivity options from IBM Watson Studio.

 

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Key benefits

  • Powerful optimization engines for faster performance
  • Quickly build optimization models using your preferred language
  • Access to more data science features

 

Register for our webinar 'Business Decisions and Resource Allocation' to learn how IBM Planning Analytics with Watson and IBMLOG CPLEX work seamlessly together. 

 

IBM Case Study:

Client:

Leading bulk tanker transportation company.

Business challenge:

To transport bulk products safely and profitably, this carrier needs to manage hundreds of constraints on tankers, drivers and cargos. How can it help its planners make optimal routing decisions?

Transformation:

This leading bulk carrier embedded IBM optimization software into its operational systems and developed a sophisticated solution that provides insight to optimize driver and route planning every 10 minutes

Results:

Millions of dollars saved annually by eliminating miles of unnecessary driving.

Millions more dollars saved annually by improving driver retention.

Million-dollar revenue boost achieved by increasing driver productivity.

Click here for more details.

 

Products

  • IBM ILOG® CPLEX® Optimization Studio
  • IBM Decision Optimization for Watson Studio
  • IBM Decision Optimization Center
  • IBM Decision Optimization on Cloud
  • IBM ILOG CPLEX Optimizer for z/OS

 

Organizational Benefits:

  • Optimized solutions to solve business problems.
  • Greater ease-of-use.
  • Comprehensive analytics capabilities
  • Movement to the cloud
  • Increased adoption beyond financial services
  • Overall market growth.
  • Open source integration
  • A flexible and scalable platform for one-to-many analytics.

Hope you have enjoyed reading this blog as much as I had testing this cool feature; stay tuned for upcoming blogs.

 You may also like reading “What is IBM Planning Analytics Local ”, “IBM TM1 10.2 vs IBM Planning Analytics”, “Little known TM1 Feature - Ad hoc Consolidations”, “IBM PA Workspace Installation & Benefits for Windows 2016”.

For more information: To check on your existing Planning Analytics (TM1) entitlements and understand how to upgrade to Planning Analytics Workspace (PAW) reach out to us at info@octanesolutions.com.au for further assistance.

Octane Software Solutions Pty Ltd is an IBM Registered Business Partner specialising in Corporate Performance Management and Business Intelligence. We provide our clients with advice on best practices and help scale up applications to optimise their return on investment. Our key services include Consulting, Delivery, Support and Training.

Octane has its head office in Sydney, Australia as well as offices in Canberra, Bangalore, Gurgaon, Mumbai, and Hyderabad.

To know more about us visit, OctaneSoftwareSolutions.

 

Business Intelligence – Business Analytics

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8 min

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This article talks about Business Intelligence and Business Analytics, things in common and about differences between one another. This blog also talks about the paybacks for an organization once these are adopted.

Let’s  start ... Though Business Intelligence and Business Analytics sound similar and are being used interchangeably by many, they do have differences.

 

Business Intelligence ( BI ) :-

Term Business Intelligence, though exist for long, have been used by wider audience from late 90s .

Intelligence with in Business comes from the data being captured. Business Intelligence has been considered as a process to collect, store, maintain, retrieve and interpret data and purpose is to optimize, streamline and smoothen current operations within the Organization. BI helps in making better-informed decisions, improve performance, helps in creating new strategic opportunities for growth, eventually helps to better understand how the business is doing, make better-informed decisions.

In bullet points, BI is:

  • A process deals with collecting data, querying, reporting, online analytical processing and alerting.
  • The purpose of business intelligence is to support data-driven business decision making.
  • BI solutions collect and analyse current and historical, actionable data with the purpose of providing insights into improving business operations.
  • Improves and maintains operational efficiency and helps companies increase organizational productivity.
  • BI is more concerned with the WHATs and the HOWs(Performance).
  • BI Technologies are efficient enough to give insight on what happened in the past / is happening right now in business –If input data to BI systems is real and granular, Organisations would have a much better insight.
  • BI refers to set of technologies(DSS) supporting decision-making process by executives, middle management.  
  • A dashboard gives all required insight needed, also displays data trend – thus helps management in taking right decision at right time to run the businesses effectively.
  • BI tools have evolved to become much more intuitive and user-friendly.

 

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Business Intelligence gives information about the data itself and also provides insights rather than making extra transformations or conversions.

Business Analytics :-

Word, Business Analytics was also in existence for long but became a buzz word in last 10-15 years. Business Analytics is process of exploring data and interpreting data.

  • Uses Statistical analysis and predictive modelling
  • Business Analytics involves multiple technologies to transforming raw form of data into a meaningful way to convey the solution in best way possible.
  • Analyses past data to drive current business and predict future business. Supports management in decision making to change existing business operations and improve productivity.
  • Helps management in improving future business operations using current and history data, thus boosting future performance.
  • Uses past data to extract insight, drive customer needs and increasing productivity.
  • Establishes trends and helps analyse WHY things are happening and provide optimized solutions to solve problem.
  • Applies to companies where future growth and productivity is one of their goal.
  • Helps in answering WHAT and WHY and also HOW to achieve.

 

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Business Intelligence and Business Analytics:-

 

Mutual:

  • Collects raw data, current and historical data. Transforms raw data into meaningful data.
  • Analyses data, helps in identifying pain points, provides alternatives, suggests optimized solutions.
  • Data mining helps in finding insights from existing data .
  • Rich visualization provides Dashboards.
  • Dashboards can be a single point to know how Organization is performing, Areas to focus, historical data visualization and many more.
  • Multiple technologies involved.

 

Focus:

  • Business Intelligence focuses on past and current data, Business Analytics also uses past and current data but helps in predicting future trend using existing data.
    • Business Intelligence Visualization helps with past and present trend to some extent predict future for the Business Model.
    • Business Intelligence uses traditional approach, Business Analytics uses Statistical methods and models.
    • Business Analytics though have reporting capability but primarily makes predictions using collected data and offers optimized solutions.
  • Business Intelligence focuses on Descriptive and Diagnostic Analytics, Business Analytics is more about Predictive Analytics and Prescriptive Analytics.

 

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Both Business Intelligence and Business Analytics share activities with in above figure.

 

Organizational Benefits:

Business Intelligence and Business Analytics helps organization in meeting their  Strategic Goals , near-term , long-term goals. These systems provide insights from raw data feed initially to a digestible and understandable information to the Executive management.

Business Intelligence helps giving insight with in Business Models, Analytics focuses on Business process and gives optimized solutions and thus helps decision making.

These systems provide details on business performance, help them answer many questions, some listed below.

    • How business is performing.
      • Current state
      • When compared to last year, last quarter, last five years etc .
      • Where is it performing well?
      • Where is it not performing well?
      • What happened?
      • Why is it not performing well ?
      • Why not ?
      • What happened?
      • What now?
      • Are we missing Goals, what/How and who, what change needed etc
    • What needs to be done ?
    • What should change ?

 

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Business Users like, Executive Team, Middle management, Business Analyst can always do ad hoc analysis, ad hoc reporting and predict the impact of change to expected outcomes. Thus, helps management in taking decisions to meet Organizational objects, goals and targets.

Hope you would have enjoyed reading this blog as much as I had testing this cool feature; stay tuned for upcoming blogs.

You may also like reading “ What is IBM Planning Analytics Local ” , “IBM TM1 10.2 vs IBM Planning Analytics”, “Little known TM1 Feature - Ad hoc Consolidations”, “IBM PA Workspace Installation & Benefits for Windows 2016”. 

For more Information: To check on your existing Planning Analytics (TM1) entitlements and understand how to upgrade to Planning Analytics Workspace (PAW) reach out to us at info@octanesolutions.com.au for further assistance.

Octane Software Solutions Pty Ltd is an IBM Registered Business Partner specialising in Corporate Performance Management and Business Intelligence. We provide our clients advice on best practices and help scale up applications to optimise their return on investment. Our key services include Consulting, Delivery, Support and Training.

Octane has its head office in Sydney, Australia as well as offices in Canberra, Bangalore, Gurgaon, Mumbai, and Hyderabad.

To know more about us visit, OctaneSoftwareSolutions.

  

Is Your Data Good Enough for Business Intelligence Decisions?

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There’s no question that more and more enterprises are employing analytics tools to help in their strategic business intelligence decisions. But there’s a problem - not all source data is of a high quality.

Poor-quality data likely can’t be validated and labelled, and more importantly, organisations can’t derive any actionable, reliable insights from it.

So how can you be confident your source data is not only accurate, but able to inform your business intelligence decisions? It starts with high-quality software.

 

Finding the right software for business intelligence

There are numerous business intelligence services on the market, but many enterprises are finding value in IBM solutions. 

IBM’s TM1 couches the power of an enterprise database in the familiar environment of an Excel-style spreadsheet. This means adoption is quick and easy, while still offering you budgeting, forecasting and financial-planning tools with complete control.

Beyond the TM1, IBM Planning Analytics takes business intelligence to the next level. The Software-as-a-Service solution gives you the power of a self-service model, while delivering data governance and reporting you can trust. It’s a robust cloud solution that is both agile while offering foresight through predictive analytics powered by IBM’s Watson.

 

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Data is only one part of the equation

But it takes more than just the data itself to make the right decisions. The data should help you make smarter decisions faster, while your business intelligence solution should make analysing the data easier. 

So how do you ensure top-notch data? Consider these elements of quality data:

  • Completeness: Missing data values aren’t uncommon in most organisations’ systems, but you can’t have a high-quality database where the business-critical information is missing.
  • Standard format: Is there a consistent structure across the data – e.g. dates in a standard format – so the information can be shared and understood?
  • Accuracy: The data must be free of typos and decimal-point errors, be up to date, and be accurate to the expected ‘real-world’ values.
  • Timeliness: Is the data ready whenever it’s needed? Any delays can have major repercussions for decision-making.
  • Consistent: Data that’s recorded across various systems should be identical. Inconsistent datasets – for example, a customer flagged as inactive in one system but active in another – degrades the quality of information.
  • Integrity: Is all the data connected and valid? If connections are broken, for example if there’s sales data but no customer attached to it, then that raises the risk of duplicating data because related records are unable to be linked.

Are you looking to harness the power of your source data to make actionable business decisions? Contact Octane to find out how we can help you leverage your data for true business intelligence.

 

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Self Service: How Big Data Analytics is Empowering Users

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Smart businesses are seeking out new ways to leverage the benefits of their big data analytics programs, and the self-service model is coming up trumps. By placing the onus directly on business users, enterprises are empowering customers with insights-driven dashboards, reports, and more. But it’s not the only bonus. 

Arguably an even greater upside for organisations is that it alleviates the talent shortage that often comes with big data. With most companies only employing a handful of data experts who can deliver analytics insights to customers, the self-service model means they are freed up to concentrate on more important tasks, while allowing the masses to derive their own insights on their own terms. 

 

What are the real benefits of self service?

If nothing else, a self-service model creates a ‘democratisation’ of big data, giving users the freedom to access the data they need when they need it most: during the decision-making process.

Moreover, there’s a low cost to entry – coupled with reduced expenses thanks to freeing up data science and IT resources – and faster time to insight. When users know what they need and can change their research strategies according to new and changing demands, they become more empowered.

But it’s not all smooth sailing – giving customers the tools they need for self service is only one part of the equation. They must also be educated on the potential pitfalls.

 

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Avoid the common hurdles

When several users have access to specific data, there’s a risk of multiple copies being made over time, thus compromising the ‘one version of truth’ and possibly damaging any insights that could be derived.

Business users unfamiliar with big data analytics are also prone to mistakes, as they may be unaware of data-preparation complexities – not to mention their own behavioural biases. 

For all these issues, however, education is the solution, which is what Ancestry.com focused on when it began encouraging self-service analytics through its new data-visualisation platform. And with 51 quintillion cells of data you can see why.

 

There’s no harm in starting small with big data analytics

Ancestry.com has over 10 billion historical records and about 10 million registered DNA participants, according to Jose Balitactac who is the FP&A Application Manager.

The old application they were using was taking hours to do the calculations.  They looked at seven different applications before deciding on IBM Planning Analytics.  

The reason they chose IBM Planning Analytics was to accommodate the company’s super-cube of data, other solutions would have required them to “break it into smaller cubes, or reduce the number of dimensions, or join members, such as business units and cost centers.” They didn’t want to do that because their processes worked.

They set up a test with IBM to time how long it took for the model to calculate and it took less than 10-20 seconds which is what they wanted. You can read more about the Ancestry.com case study here.

If you’re keen to empower your business users through a self-service model, contact Octane today to learn how we can help you harness big data analytics.

 

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Planning Analytics and PowerBI

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Many businesses have already turned to Octane and partnered with us to help turn their data into meaningful insights. So if you've wanted to connect your Planning Analytics to Power BI, you're not alone and now with us you can.
 
Octane has developed a way you can work directly with IBM Planning Analytics (powered by TM1) and Microsoft Power BI! We've had a number of clients who have wanted to integrate Planning Analytics and Power BI without using external proprietary software we at Octane can say that we've answered the market's call.
 
Planning Analytics powered by TM1 is one of the worlds most popular tools for data consolidations and forecasting whilst PowerBI is one of the most popular data visualsation tools and now Octane can provide you a One-stop solution which includes the data import from tm1 with metadata information about the data hierarchy.
 
Gone is the need for writing TI processes to create a csv file output, then read the csv file and load the data into PowerBI or any number of other permutations that require several more steps/operations which is time consuming and costly to your business. 
 
With the power of a Restful API solution Octane Software Solution will connect TM1 and PowerBI. See how simple it is!
 
Contact bidesh.pal@ocatnesolutions.com.au for a commitment free demo today. 
 
 
 
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IBM Cloud Private for Data is AMAZING!

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Yesterday I was fortunate to attend an IBM partner day based on IBM Cloud Private for Data or ICPD and thought to write this blog for you who might not know what the platform is or does. 
 
 
This platform balances the individual data needs of your business by providing an integrated self-service, agile, enterprise-ready platform to significantly improve the governance around the collection, organisation, dissemination and analysis of your data but this isn't the best part. ICPD utilises modern microservices applications to enhance your data assets and analysis with machine learning (ML) and artificial intelligence (AI).
 
Built on the foundation of IBM Cloud Private, ICPD is an integrated end-to-end platform designed to help make data more accessible and trusted across your organisation. Further, the platform facilitates the inventory and cataloguing of data sources, the platform then provides further access to many analytical tools to gain insights from your data then easily share, request and approve access, otherwise governing this across the enterprise.
 
With this level of governance, transparency and armed with insights from data the platform then facilitates the fast development, training and deployment of ML and AI models. Personally having worked through these models yesterday and well into the night I was blown away with the capabilities of the ICPD platform.
 
The result of the aforementioned and what I think everyone should pay attention to is that this is a single platform to achieve what many enterprises set out to do. That is to provide high quality; trusted data that can be more easily prepared, collated, secure, analysed and disseminated all in a single integrated platform.
 
But how can this all be managed you ask? Well it can be managed internally or by a third party. It can be hosted on-premise or externally on the cloud with many options to customise the experience and the requirements you have to fulfill your unique needs and security requirements.
 
I can only say so much... but I'm simply amazed, to say the least. Click here for more information 
or experience it for yourself, a white paper is also available for you below.
 
Download ICPD Whitepaper
 

Expand your Business Value with intelligence and Analytics

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Forward-thinking enterprises are using business intelligence and analytics to navigate through their digital transformation – one that could see them expand both physically and fiscally at a rapid pace.

The ability to harness this data and use it to make business decisions, however, poses challenges. This is particularly true for organisations that haven’t previously had the technology nor the manpower to sift through all the historical data they’ve accumulated in their daily activities.

Think about it – by 2020 it’s predicted there will be 5,200GB of data for every individual on the planet. More importantly, 90% of our current data is ‘unstructured’, drawn from mediums like social media and Internet of Things (IoT) devices.

 

Harnessing the power of business intelligence and analytics

So how can enterprises take advantage of data analytics? It starts with the three ‘I’s:

  • Investment: Collecting and analysing your company data for future activities. You might be surprised what sort of data can be used for predictive modelling purposes to forecast future trends and outcomes.
  • Innovation: Harnessing unexplored or raw data to see if your business can create new products or services.
  • Improvisation: Parsing business data to find new meaning in it. This can lead to actionable insights that feed into the analytics cycle.

 

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Using data to generate value

More and more businesses are recognising the usefulness of their data, but there’s still a way to go. While more than 80% see AI as a strategic opportunity, most aren’t using data to its full advantage.

These organisations may be using AI to reduce their operational costs or modernise their systems for better business intelligence and data warehousing, but to derive the very best value from your data, you need to be an insights-driven and transformative company.

 

What is the analytics cycle?

This all feeds into the analytics cycle, of which there is no one-size-fits-all design. IBM, for example, defines the cycle as taking a Planning, Descriptive, Diagnostic, Predictive and Prescriptive approach.

But the bottom line is that every business’s analytics cycle should feed into one central goal: gaining a competitive advantage.

Your cycle might start with identifying a business problem, preparing and analysing the data, A/B testing different solutions and then monitoring the results.

And that model will likely change according to the data you are analysing or the business problem you need to solve. So long as the target is building a more sustainable and competitive business, you’ll be able to use your business intelligence and analytics to generate greater value for your company.

Are you looking to harness the power of data and rise above the competition? Contact Octane Software Solutions today to find out how we can help you derive real business value from your data.

 

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