Planning Analytics Administration: An Alert (Proactive Mechanism)

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.

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.

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.

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

Source : Planning Analytics (TM1) cube

print    

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

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.

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.

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.

print    

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.

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.

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.

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 ?

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.

print    

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.

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.

print