The decision pipeline: The shared experience (4 of 6)

This is the fourth post working through the Sabisu decision pipeline. We’ve looked at Data Acquisition, Analytics and Visualisation:

6-box-model-box-3-features

The next step is the reason we created Sabisu; the platform pivots around this question:

“How do you share that story?”

That’s the question that no one else was asking; with data, a story and reports that support each other, how do you ensure everyone has the view they need?

It was looking at the implicit assumptions (data, story, reports) that took us back to putting in place Data Acquisition, Analytics and Visualisation.

This is where most solutions quit. This is where we started.

Distribution is about ensuring everyone has the view they need.

For most of our customers email is the tool of choice; sending an MS Excel file, or a link to a file store. We see a lot of MS Excel documents…with lots of links and macros…that usually only one person knows how to maintain.

Users are often not looking at the latest data. It’s a pain to maintain. Multiple versions abound. It’s absolutely not one version of the truth and often, meetings are dominated by working out if the report is valid, rather than making a decision.

More mature customers sometimes have a URL to a dashboard, which is great. Distribution does not mean sharing a URL to the same view; it means everyone has the view they need.

Sabisu is all about the ‘shared experience’. It’s not a new thing to us; it’s driven platform development for the last 4 years.

In summary the Sabisu shared experience is about:

  • Each user seeing their own view of the situation, with the tools and visualisations they need to bring their expertise to bear
  • Multiple collaboration options – in-line, seamlessly accessible and unified across the platform to ensure expertise is shared
  • User and community autonomy, so that users can control the growth and ensure relevance to all users.

This is all about Distribution, which encompasses easy sharing and always links back to trustworthy data. Everyone sees the same story – even if their precise view might be optimised to their needs.

We wanted to decisions to be made referencing appropriate, current data, rather than not getting made at all or getting made and not sticking. We wanted customers to be debating the decisions, not whether the data was up to date, recognisable, trustworthy or tracking reality.

This takes us to here on the journey:

6-box-model-box-4-features

In the next blog post we’ll look at a key step; once everyone has the view they need, how do you turn that into money?

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We’re always interested in hearing from you with any comments or suggestions, feel free to get in touch.

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New Statistical Analysis Tools

As customers have started to use the Widget Working View introduced earlier this year, we’ve seen the need to grow the analytics library to help users quickly assess or identify interesting events/periods in their data.

We’ve added Flat-line Detection, Rate of Change and Breach Limit Forecasting. Here’s how they look in action:

 

signal-status

These new analytics functions in Widget Working View are categorised under the ‘Signal Status’ heading:

Flat-lining:
Shows where data is remaining at a continuous low level. Users can adjust the tolerance to fine tune the charts where signals have slight but important variation.

Rate of Change:
Shows if the series on the shown chart are changing faster than the given hourly rate. This is great for separating out curves that may look similar but actually are varying at a very different rate. Also it allows a potentially catastrophic rate of change to be immediately identified.

Breach Limits:
Shows if any of the series will breach a given limit at their current rate by a point in the future assuming the series behaviour is similar to that on the chart. The future point is equal to an additional 30% of the chart x-axis; in the example below it’s 5:09 pm on the same day.
(If the x-axis was 10 hours, the forecast would be for the 13th hour.)

Find out first

We continue to update and add new functionality into the Sabisu Platform each week, if you’d like to keep more up to date and be the first to hear about new product releases and beta tester opportunities why not sign up to our newsletter. Click here to sign up now.

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We’re always interested in hearing from you with any comments or suggestions, feel free to get in touch.

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Little signs you’re getting the culture right

“Can I have half an hour on the agenda?”

Adam gets his half an hour, no questions asked.

We get the whole team together a couple of times a year for a ‘team day’ to review technology, R&D or processes. It’s always interactive. Time is precious.

Asking for a half hour slot with the whole company is a declaration of importance and relevance. Adam knows that.

Everyone knows that.

Surprises are precious. We find little surprises everywhere; in projects, new requirements, little bits of functionality people have created to improve the customer experience. Most companies don’t like surprises but we do. They keep us interested. Drive us on. They’re important. Adam knows that.

Everyone knows that.

It’s so easy to check with the boss. It’s safe. Reduces the chance of failure or embarrassment.

If your people are checking everything with the boss, it means they’re afraid of failure. It slows things up; you don’t get to see the riskier/more unlikely ideas; they’re going to play it safe.

Your customers will only ever see exactly what they ask for, rather than what you can do. It’s ok to take a risk. Everyone knows where it’s appropriate.

Everyone knows that.

These things aren’t stated in some company handbook. The day to day important things never are. They’re just things everyone knows, defined and reinforced every day with small affirmative actions.

That’s culture. Matt Busby knew all about building a culture:

“Do the small things, do them well, do them now.”

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We’re always interested in hearing from you with any comments or suggestions, feel free to get in touch.

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Quick and easy data manipulation: Pipeline meets Widget Working View

We’ve married two of our best analytics tools which means you can manipulate, analyse and configure your data faster than ever.

Once you have created a widget in the Pipeline Directory you can reconfigure the pipeline directly within Widget Working View.

This means you can make changes on the fly with instant results seen in the Widget. If you want to compare more data, this data can be easily added to the Pipeline and the Widget will update with immediate effect.

This gives end-users increased flexibility when fine tuning reports or interrogating data. End-users can now easily configure Sabisu. Examples of such configurations include;

  • New reports shared onto Community dashboards
  • 24/7 monitoring & alerting of key process/project metrics and KPIs
  • Automated responses, e.g., alerting users or initiating an investigative process
  • View, analyse and interrogate your data with optimal effect
  • Identify problematic trends in projects, and be instantly alerted when quality indicators fall outside limits or identifying repeated issues in production.

pipeline-on-wwv

 

We continue to update and add new functionality into the Sabisu Platform each week, if you’d like to keep more up to date and be the first to hear about new product releases and beta tester opportunities why not sign up to our newsletter. Click here to sign up now.

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We’re always interested in hearing from you with any comments or suggestions, feel free to get in touch.

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The decision pipeline: Show…don’t tell (3 of 6)

This is the third post working through the Sabisu decision pipeline. In the first post we looked Data Acquisition and in the second, Analytics.

So we’re here on our journey:

6-box-model-box-2-features

With the data now telling us a story through the Analytics used, it’s relatively easy but pretty useless to dump a large table of numbers in front of someone. The human brain is excellent at recognising 2D patterns, so the next challenge is Visualisation.

MS Excel and other reporting tools can give simple bar, line or scatter charts but they’re only good to a point; the toolbox is limited, the tools don’t scale. Try using MS Excel to generate many graphs, or use lots of process data and you’re in a world of pain.

This makes creative reporting difficult. It’s also too easy to create ineffective visualisations. The challenge is to show the story so users can infer the decision possibilities.

Sabisu handles visualisation differently to other applications:

  • Users are deliberately limited to visualisation choices that make sense. This means every Sabisu page is consistent, high quality and easy to use.
  • All the Sabisu standard visualisations are clear, fast and flexible. No other solution can fill a screen with visualisations that perform like Sabisu’s.
  • Each visualisation is user customisable directly in the front end.
  • Each visualisation has analytics in-line, responsive, real-time, so that users can interrogate data to find or refine the story. There’s no waiting for numbers to be crunched.
  • Collaboration is seamless, with Notes, Actions and Advisories all in view against the relevant data. After all, what’s the point of this data if you can’t action it in some way?

Sabisu does give power users and developers more options, through the Sabisu Chart library and external libraries such as D3.

This takes us to half way in our journey:

6-box-model-box-3-features

 In the next blog post we’ll look at a key step; once you have your story, how do you share it?

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We’re always interested in hearing from you with any comments or suggestions, feel free to get in touch.

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Analytics for everyone all the time with our new in-line working view

We are pleased to introduce Mini Widget Working View (MWWV).

The Widget Working View (WWV) has been really popular with end-users as it allows them to zoom into a widget and analyse, configure and collaborate on data in a single, easy to use interface.

How could we make it better?

Sabisu now allows any user to put that analysis, configuration and collaboration alongside every widget on a page – no matter how many widgets or parameters there are.

There’s no need to zoom into the widget; you can view several widgets, with Notes appearing alongside in real time and inline analytics appearing on the charts.

You can also now change the x and y axis on your charts within WWV. For example you could view a years worth of data or switch the axis to drill down to a monthly view.

Whatever your analytics or configuration preference it will now persist when published to a Community so you can share your view with others.

mini-widget-working-view

 

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We’re always interested in hearing from you with any comments or suggestions, feel free to get in touch.

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Sabisu Project Analytics: Automatic schedule task ordering using risk profiles

Recently we’ve been telling customers about our project schedule analytics, which is already in use on some innovative megaprojects and large capital projects with significant engineering challenges.

A really nice value-add feature we’ve been able to add is automatic task ordering – a valuable thing to have if you’re working on a plan with many thousands of tasks.

With such plans it’s not always easy to sequence the tasks optimally and it’s impossible to get an objective assessment of how well sequenced tasks are.

Also, it’s hard to judge the efficiency or risk profile of a plan provided by a third party.

Sabisu analytics can help.

Sabisu will now automatically sequence tasks according to connectivity and risk profile. It front loads tasks with the highest risk level to create the most resilient plan it can, minimising the risk of schedule over run later in the project life cycle.

Sabisu recommends when the task should be started and in which order.

task-ordering

Clearly this can only be an aid for an experienced scheduler. Indeed, the modification of the plan is left to the scheduler so they can bring their knowledge to bear and ensure that the plan is workable.

We think this is another powerful addition to Sabisu Project Analytics. Look out for more soon.

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We’re always interested in hearing from you with any comments or suggestions, feel free to get in touch.

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Introducing Sabisu Project Schedule Analytics – Reduce risk, plan effectively

Project planning is a technical and complex process, particularly on large capital projects with complex engineering challenges. With Project Analytics, Sabisu can now assist project planners by analysing the viability of a project schedule and advising on task sequencing where there are dependencies that could stop a project in its tracks.

Sabisu takes XER files from Primavera and analyses the dependencies in the schedule using network mathematics to arrive at connectivity based risk profile. This is condensed into a single ratio to make qualititative assessment easy; a high connectivity ratio indicates a high risk plan.

project-analytics

There are three types of analysis applied to the project schedule:

Node Rank: This ranks tasks by how much risk they introduce to the project. Node Rank doesn’t just look at neighbouring tasks – it builds a picture of all the successor tasks to identify tasks with high downstream connectivity, i.e., with a lot of successor tasks. it builds a complete picture; successors, successors of successors, and so on through to any number of orders.

A high degree of onwards connectivity means that a delay to a task would affect many other tasks and so is ‘riskier’.

Page Rank: This ranks tasks according to it’s predecessor tasks. Like Node Rank, it looks backwards all the way through the plan to build a picture of all the predecessors that could affect the task.

Therefore a task with many predecessors is at risk; if any of the other tasks are delayed this task would too be delayed. This could introduce risk into the project.

Risk Nexus: Shows tasks that are both dependent on other tasks and have other task dependent on them, i.e., with both a high Node Rank and Page Rank.

This could be thought of as a ‘bow tie’ model as seen in health and safety risk or failure mode analysis; a single point of concentrated risk in the schedule.

cluster-diagram

This is best represented using Force Directed Graphs to depict a tasks connectivity network. In the example above the red node in the graph is the selected task. Displaying the tasks in this way allows you to explore and visualise dependencies.

 

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Sabisu Actions – Attaching Files and better filters

Sabisu Actions continues to be a differentiating and popular feature, so great news for dedicated users with some useful updates.

Two important features have been added in this release:

  • Attach a file
  • Improved filters

Attaching a file to an Action is now possible.

There’s an infinite list of possible uses; it could be a form you need someone to complete, an important document they need to read or a photograph of a health and safety hazard that needs actioning.

actions-uplaod-files

We’ve made it easier to search through actions by adding in two new filters; category and tag. The filters will then group your actions together making them easier to find and view.

action-filters

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We’re always interested in hearing from you with any comments or suggestions, feel free to get in touch.

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The decision pipeline: What’s the story? (2 of 6)

This is the second post working through the Sabisu decision pipeline. In the first post we looked at why we created Sabisu and how the story starts with Data Acquisition.

So we’re about here:
6-box-model-box-1-features

What follows acquisition?

How long have you spent crafting MS Excel reports, creating complex macros to work out what the data is trying to tell you?

How long have you spent waiting for MS Excel to open because the quantities of data are so large?

What do you do when the datasets are so large there’s no way of processing them?

The answer is the second step on our data pipeline journey: Analytics, which encompasses data transformation, processing, calculations and algorithms.

The Sabisu approach is to build horizontal analytics; algorithms which will apply to many different processes and use-cases, which necessarily means statistical techniques are to the fore.

We integrate them right into the end-user’s line of sight – as soon as they maximise a chart, there’s a full array of statistical process control techniques to hand, e.g., Edge Correlation, which can provide simple prediction of incipient issues:

edge-of-correlation

Or perhaps anomlous signal behaviour detection, used to identify when an asset failure mode initiates or a project characteristic changes:

spc

You’ll find plenty more in the platform including curve fitting and dynamic warping, used to determine whether a situation is recurring, or a repeated process is running optimally:

time warp

There are also some specific analytics we run in Spark/Hadoop, or Python, or C++ engines (e.g., alarm analytics).

There are many analytics platforms out there – but beware the ones that merely draw graphs.

We’re always looking for better ways to find the story in the data, learning from data journalism (with the fabulous Guardian people) and other sources.

So we’re here in our journey:

6-box-model-box-2-features

Analytics is not visualisation; many tools skip analytics and draw pretty graphs with data. Visualisation is where we’re going in the next blog post.

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We’re always interested in hearing from you with any comments or suggestions, feel free to get in touch.

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