Sabisu Release: Timeline Buckets

This week we’re excited to share some new additions to the Timeline functionality in Sabisu.

The Timeline now helps you decide which updates you need to see, or need to take action on.

Timeline Buckets

On entering the Workplace Timeline or the Community Portal, you’ll notice we’ve added some new items on the left hand side. These are our new Timeline Buckets, which sort updates into handy categories so you can quickly see what’s new.

Using these simple filters you can view what’s important to you and navigate to it directly, e.g., Actions you’re due to complete, or alerts notifying you of anomalous process behaviour.


Clicking on any individual Bucket will give you a breakdown of the latest updates and changes. Let’s look at each Bucket and what you can expect to see.



Sabisu collects all your data updates in the Data bucket. You’ll see notifications of widget updates and updates to Sabisu Go publications.



analyticsThis bucket shows what your data is telling you, such as:

  • Anomalies detected
  • KPIs which have breached their limits
  • Automatic escalations from systems such as Jidoka


distributionDistribution will let you know of any Community updates or commitments.
You’ll also see updates to Actions that are visible to your Communities.


actionDriven predominantly from Actions within Sabisu, you’ll see anything you’ve been assigned and is due for completion in the upcoming week. Any Actions which are overdue for completion will also be available here.


changeAggregates recommended or completed changes, e.g.,:

  • Advisories generated by Sabisu
  • Changes to rules or calculations in Jidoka
  • Manual escalations

Interacting with your updates

One of the major new features which comes as part of the buckets is allowing you to interact with any of these updates and navigate to where it’s happening. For example, you might have an Action to view, you can click through to view the full detail in the Actions system. Likewise clicking on Widget updates will take you across into your Workplace to view that particular Widget.

What about Notes?

You’ll know all about Notes: they’re truly multi-purpose, with users leaving Notes against widgets, data points in a graph, parameters/KPIs, Community pages or against Communities themselves.

Therefore Notes appear in the timeline but not under any specific bucket. They’re also directly accessible through the header bar ‘Notes’ item.

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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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What happens when you upload a file in Sabisu Go?

Sabisu Go is a small app that allows you to quickly and easily integrate any and all of your data into Sabisu reports and analytics.

Most of our customers are heavily reliant on MS Excel. It’s a powerful tool but leads to proliferation; lots of conflicting reports means reconciliation is difficult, reports are inconsistent and not credible.

This applies to all MS Excel; from project to process data; from Primavera to AspenTech IP21 and OSISoft PI.

Publishing reports using Go eliminates proliferation, centralising all your reports and their underlying data around user communities.

Every time you publish using Go, automation kicks in to make your life easier – both now and when you come to report in 6 months, a year, or 6 years.

Every time you update Sabisu, this happens:

What happens when I upload an Excel file



In practice, this results in:

  1. Instant reports
    Sabisu will create default reports automatically. It’ll even add them to a page ready for you.
    Starting as a table, you can quickly format a variety of chart types and share them. The idea is to give you something for nothing; a simple visualisation for zero effort.
  2. Versioning
    Every update is stored in its entirety as a new version.
    Everyone can see the latest update – unless it’s version controlled.
    As soon as you version control an upload, all future updates are also version controlled, meaning you can hold data for review and approval before updating the Sabisu platform for your users.
  3. Data extraction & storage
    Every update is decomposed into our noSQL datastore in its entirety.
    Sabisu stores all of it, just in case you decide way down the line that it’s important, or you want to calculate KPIs using significant quantities of historical data.
    So that’s all your MS Excel spreadsheets stored for eternity.
  4. Calculations & KPIs
    Sabisu decodes your spreadsheet formulae to automatically construct calculations.
    These calculations can then be used to drive visualisations across the platform. Or you can use the analytics capability to build a story around them.
    These calculations will update continuously as the data behind them updates automatically – without your intervention.
    Sabisu will also create the connections necessary to any back-end systems used in formulae within MS Excel, so that when you’re ready to move to a real-time system without MS Excel, it’s ready to go.

Sabisu Go allows you to publish your reports and their underlying data into an easy to use, enterprise grade reporting dashboard.

It solves your consistency, communication and presentation challenges.

Go integrates MS Excel, OSISoft PI, AspenTech IP21 and via ODBC/OLEDB almost any data source.

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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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Financial Functions in Sabisu

Many decisions are motivated by cost, and the effectiveness of decisions is often defined by their impact on profitability. Sabisu now provides easy to use functions that can help with finance related choices, such as calculating project/investment feasibility, and determining which new asset option represents the best value for money.

Net Present Value

Net Present Value (NPV) is a method to evaluate the current worth of an investment based on a series of annual cash flows. It is commonly used to evaluate the financial feasibility of projects.

NPV incorporates the time value of money (TVM) into its analysis. This principle describes the greater benefit of receiving money now rather than later, and explains why interest is paid or earned. As an example, if you were to deposit £100 into a savings account for one year it is reasonable to expect that you would increase in value due to interest. At an interest rate of 5% the initial deposit will be worth £105 in one years time (£100 x 1.05). Put simply, £100 received now is worth £105 in one year.

Conversely, if someone promises you £100 in one years time the value of the money right now is not £100, as if you had it right now you could invest it and earn interest (at 5% if we deposit it in our savings account). The value of the money today is £100 / 1.05, which is £95.24.

NPV uses this TVM principle to determine the present value of a set of annual cost/benefit streams. If the resulting NPV is positive, then it means that the investment will add value to the business. If it’s negative then it would subtract value and the project should be rejected.

Investment Opportunity – An Example

Barry is very carefully considering a proposal from a small external company who wish to partner with his firm to produce a new product line. To do this Barry’s firm would invest £10,000 to set-up the necessary machinery, the external company would then manage production and promise £3,000 each year for 4 years.

To Barry this seems like a feasible project, with a £2000 profit after 4 years giving a 20% return on investment. However, he takes it to the finance director for verification.

The finance director immediately spots Barry’s mistake, he hasn’t considered the time value of money. The £3000 that the firm will receive at the end of the year isn’t worth £3000 today, and the £3000 at the end of year 4 is worth even less! This all needs to be offset against the initial investment.

The finance director calculates the Net Present Value of the project using a discount rate of 10% (this is usual, as the discount rate not only accounts for interest that could be gained from a bank, but also other factors such as changes in inflation). The NPV of the project is found to be -£490.40, which means that under the suggested agreement their firm would lose money.

There are, however, many ways in which this deal can be changed slightly to give a positive NPV, making it a good investment. For example, the agreement could be extended by a year, the external company could pay £4000 in year one (or any other year), or Barry could simply ask for a higher amount each year. NPV can be used to formulate these plans, and order them in terms of profitability.

Equivalent Annual Cost

Equivalent annual cost (EAC) is the effective cost, per year, of an asset. It can be used to compare the cost effectiveness of various assets over different periods.

If a new asset is needed there are often different options available. Some will have a smaller initial cost, but may cost more to maintain, or have a shorter lifespan compared to higher priced equivalent assets. EAC can be used to evaluate the cost effectiveness of these different configurations so that the best decisions can be made

Choosing a New Compressor – An Example

A new compressor is needed for a chemical production plant. Two options have been presented:

Compressor A Compressor B
Initial Cost £60,000 £140,000
Lifespan 4 years 9 years
Annual Maintenance Cost £12,000 £7,000

This is clearly not a straight forward comparison. Compressor A is less than half the price of Compressor B, but has a shorter lifespan and higher maintenance costs. EAC provides a method to compare the equivalent costs of the two assets, a discount rate of 5% is used to represent the cost of capital:

Compressor A EAC = £28,921
Compressor B EAC = £26,697

The EAC tells us that the annual cost of Compressor B is lower than Compressor A, meaning that  even though the initial cost is higher, Compressor B is the sensible option.

Sabisu Finance

Here we have introduced some of the new financial functionality within Sabisu, using examples to show where they can be used. Sabisu makes these techniques easy to apply, requiring only the most basic information. This ensures efficient and effective decision support for a wide range of project and budgeting scenarios.

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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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Storytelling with Data Analytics

This is the age of data, and there are few places where this can be seen more clearly than the process industry. The classic problem has always been extracting the useful information from this vast sea of numbers. Sabisu encapsulates a large range of advanced analytics tools that make this information extraction simple. However, with more and more data being generated from a growing range of assets a new problem has arisen: From all of the extracted information, how do I determine the underlying story?

The story in the data is not simply the information it contains, but rather:

The subset of connected information that leads to the determination of a meaningful time line of events.

That is, the pieces of information that are related to each other can be extracted to describe a specific event, often in a coherent chronological order. Sabisu provides some important analytics tools to help elucidate this time line.


A plant manager is concerned that the overall cost of operation has increased substantially over the past week. They ask an engineer to investigate.

The engineer could approach this by going directly to the anomaly detection system. However, there are a large number of assets, and it could take a long time to find one with with a high anomaly rate. Furthermore, how would they know that this was the asset that caused the problem? It could be an entirely unrelated issue.

A sensible starting point for the engineer is Jidoka, which tells the engineer that steam usage is much higher than it should be, and is the current highest contributor to the price of non conformance (PONC) for the plant.

Knowing that something must be underlying the increased steam usage, the engineer turns to correlation analysis. Cross correlation will identify signals that are related. By correlating other process data to the increasing steam curve, it may be possible to identify the section of the process that is responsible for the rising operational cost.

Plant Cost

The cost of operation for the plant. Notice the significant rise towards the end.

It is found that an increase in product viscosity “predicted” the increasing steam usage. This means that it is highly probable that the higher steam usage is due to this increase in viscosity. This can be seen by the off-centre peak in the correlation plot.

The cross correlation of product viscosity with steam usage. Notice the off centre peak, which indicates that the viscosity increase is the cause if the rising steam usage.

The cross correlation of product viscosity with steam usage. Notice the off centre peak, which indicates that the viscosity increase is the cause if the rising steam usage.

The engineer knows that an increase in steam usage is due to the automatic process controller opening the steam valve, something it would do to compensate for a higher product viscosity.

Now that the engineer knows that the underlying problem is related to product viscosity, they know what section of the plant is likely to be the cause of the problem. By looking at the anomaly detection results for the assets in this section the engineer notices an increasing number of anomalies on one of the centrifuges over the past few days. This increase in the number of anomalies precedes the increase in product viscosity. They order a deep clean of the centrifuge and set-up automatic anomaly notifications for all of the centrifuges to alert them if a similar problem arises in the future.

Anomaly detection results for the faulty centrifuge. Notice the high anomaly arrival rate (indicated by the red lines), and the fall in output towards the end.

Anomaly detection results for the faulty centrifuge. Notice the high anomaly arrival rate (indicated by the red lines), and the fall in output towards the end.

The Story

The story behind the data is that a problem with one of the centrifuges caused its output to fall. This lead to an increase in product density, which was compensated for automatically with an increase in steam. This rise in steam usage caused the operational costs to increase.

Anomaly detection has been configured to alert the engineers if a similar problem occurs, this will prevent the chain of events from occurring again.

The Order of Information

The order in which information is presented is key to effective communication. It is often tempting to work chronologically, summarising each step taken to reach the conclusion. However, this leads to the most important information being reached last. Instead, it is much more effective to present the key conclusion first, followed by supporting information.

In the case of our example, the engineer may report back to the plant manager. The key information is:

  • The cause of the increased costs was tracked to an under-performing centrifuge.
  • This asset has been cleaned to rectify the problem.
  • Anomaly detection has been configured to provide early warning of a similar event, which will prevent its re-occurrence.

These three pieces of information may well be all the manager needs to know. It explains the root cause of the problem, and shows that it has been rectified and should be prevented in the future.

Data Stories in Sabisu

Storytelling with data analytics is the most efficient way to extract the truly useful information from your large datasets. Sabisu provides a range of tools to simplify this process, and helps you display and share this information throughout your organisation.

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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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Visualisation Update

As you’ll know we’re continually upgrading Sabisu, adding new capabilities and making it ever more powerful and easy to use. The visualisation technology in the platform has been carefully selected & evolved over the last 5 years.

Whilst legacy approaches (e.g., static server-generated charts) remain supported in the platform most visualisations are using HTML5, CSS3, D3 and Sabisu libraries developed using these technologies.

The three main approaches are to use libraries from Google, our own Sabisu libraries or D3.


Google Charts Examples

Google’s charting and web toolkit remain key for many implementations as they’re quick and easy. They’re perfectly suited to simple applications requiring limited interactivity.

About 50% of Sabisu components use Google Charts (a number that’s reducing as we cut over to Sabisu libraries).

Where interactivity is essential or more advanced features are required, Sabisu has it’s own charting libraries which use HTML5, JS & CSS.

Pros Cons
Good for simple charts Data volumes
Ready to use Performance
Flexibility & development

Sabisu Libraries

Sabisu Chart Examples2


Sabisu has it’s own charting libraries which use HTML5, JS & CSS3.

These were developed to add interactivity and deliver increased performance, particularly with large datasets. They integrate better with the various analytics capabilities within the platform and they’re as easy to use as Google Charts.

About 20% of Sabisu components use Sabisu charts (a number that’s increasing due to the performance & interactivity advantages).

Pros Cons
Flexibility None
Custom visualisations




Alarm Management - Cluster FDG (1)

Where very complex intereactive visualisations are requried, Sabisu uses the D3 library,  a JavaScript library for manipulating documents based on data. D3 uses web standard technologies; HTML, SVG and CSS.

D3 is an excellent choice where heavy customisation is needed. It’s powerful and flexible, but therefore requires developer and/or mathematical skillsets. There is a slight overhead in terms of client performance as the D3 libraries are larger than the Sabisu visualisation libraries.

Therefore around 10% of Sabisu components use D3 charts – usually for specific visualisations of complex analytics.

Pros Cons
Flexibility None
Complex visualisations
Custom visualisations

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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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Anomaly Detection in Sabisu

Anomaly detection of time series data is a complex problem, especially when the data is subject to measurement error and noise. First you must decide what constitutes an “anomaly”, usually through the construction of a statistical representation of past behaviour. New data is then compared to this statistical model to identify significant changes in behaviour. The anomaly definition is important, as if it is wrong important failures will be missed, or normal behaviour will be flagged as anomalous. Take the signal in Fig. 1, it might be obvious that point A should be classed as an anomaly, but what about point B?

Example time series with anomalies

Fig 1. Example time series with a clear anomaly (A), and a potential anomaly (B).

Sabisu’s anomaly detection system can be used to quickly and reliably identify points of change in time series data. This means that it can be applied to most process data collected on a plant. However, when combined with metasensors®, you have a system that can intelligently determine when something is going wrong anywhere on the site.

Sabisu uses a combination of in-house and cutting edge algorithms to build a statistical picture of your data in real time. It is possible to use these algorithms individually, or combine several of them through a quorum voting system to account for signal noise and variance.

The algorithms can be finely tuned to your process data, or more commonly the metasensors® encoding this data. The application of anomaly detection to metasensors® provides a convenient way to monitor assets, detecting faults at an early stage. The anomaly detection system will then automatically notify the relevant Sabisu users of the potential problem. For continuous monitoring the system can also send regular digest emails summarising the number of anomalies detected across the plant assets.

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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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Monitoring Assets with Metasensors®

“Mr. Wallace sees, at a glance, that he may as well try to find a lost shell on a sea-shore, or a needle in a haystack, as attempt to discover what he is desirous of picking out of this documentary chaos.” 

— Charles Dickens, Household Words (Vol. 2)

A typical industrial plant is made up of many different physical units such as furnaces, compressors, and pumps. These units can be divided into smaller components such as heat exchangers within furnaces, which can then be subdivided further and further until you have the base components of valves, tubes etc. It is at this low level that most sensors operate, reporting the pressures within each pipe, the temperature of each component and the status of every valve. It is usual to have dozens if not hundreds of sensors monitoring every aspect of a plant unit, which leads to thousands upon thousands of sensors monitoring the plant as a whole. This fine level of detail is the most accurate (and possibly only) way to measure a plant in the physical sense, but generates so much data that is presents challenges that are rarely addressed satisfactorily: How do I monitor my plant? What’s important? Where do I look?

It is clear that the number of sensors is so large there is no way to monitor each one, so often this is approached as a simple prioritisation problem. Find the most “important” sensors for each asset, decide that’s still too many to monitor, so pick your favourite and hope for the best.

The wealth of rich sensor data that is collected is then relegated to the role of disaster analysis. That is to say, something went wrong, why? After a failure process engineers go through the recorded sensor data for the failed asset to determine what went wrong, often finding that if they had been monitoring the right sensors the fault could have been identified in time to do something about it before it failed.

In an ideal world you would be able to monitor all of these sensors, identifying the early signs of failure without needing a team of people to stare at thousands of sensor graphs day after day. Sabisu has developed a way to do just that.

Metasensors® are a higher level sensor construct that combine a number of real sensor time series into one. This is done in such a way that mathematically guarantees the minimum loss of information, ensuring that the important information is encoded in the metasensor®. Rather than the base level covered by physical sensors, metasensors® can be built at the asset level, with a metasensor® for a whole heat exchanger made up of its constituent components.

Using this technique it is possible to effectively monitor a whole plant. With metasensors® operators can monitor the plant at the asset level, and be quickly directed to the important sensors when something goes wrong. Sabisu’s system goes one step further, and actually performs the monitoring for you! This is achieved through Anomaly Detection – which we’ll tell you all about in our next 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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Introducing Sabisu Go: Build enterprise dashboards in under 3 minutes

We’re delighted to announce that Sabisu Go has today been made available to selected beta testers.

Go is a lightweight client SaaS install which allows you to quickly publish data to Sabisu.

With Go, we want to bring Sabisu analytics to everyone, everywhere, with any data. So we’re launching with MS Excel, OSISoft PI and AspenTech IP21 integration. There’s more to come.

Sabisu Go_beta_20160310

Using Go, you can build an enterprise dashboard in under 3 minutes. Here’s how:

  • Select the data you want to use.
  • Tell Sabisu where it should go.
  • Sabisu automatically…
    • Version controls your data
    • Processes your data, historising it for future trending
    • Creates all the KPIs you need
    • Builds your dashboard
  • Test & modify your dashboards as you like using our templates.
  • Share it with colleagues, JV partners & your whole value chain using the full range of Sabisu collaboration tools.

Key features:

  • Go will work anywhere, for anyone: it’s a pure SaaS install
  • Go integrates data from anywhere, starting with MES / historians and MS Excel
  • Automation & seamless workflow means it’s fast and easy to use
  • Go is bi-directional; it takes notifications and updates from Sabisu to your desktop, meaning you’re up to date
  • Go’s automated analytics generate all your KPIs and historise all your data without you lifting a finger – everything you need is ready and waiting
  • Go’s APIs can take input from any data source – watch this space

Sabisu Go_beta_20160310_2


Go will be released as a generally available beta 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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Why implement a hybrid-cloud or cloud-tethered solution?

Sabisu is unusual in that it was designed from the very beginning as both a cloud and on-premise solution. Sabisu’s hybrid-cloud and cloud-tethered architecture delivers new capabilities ahead of customer demand so that customers can realise competitive advantage.

Designing the platform in 2010, the team was fortunate that cloud technology was sufficiently mature and the likes of Facebook and Google had demonstrated some compelling value propositions, engineering and business models.


So why take on the cloud? And why keep the on-premise? What’s the difference between hybrid and cloud-tethered? And how does it work?

Why cloud?

  • Supports a zero-IT SaaS rollout in a matter of hours, disrupting traditional enterprise IT models
  • Direct channel for communicating data to vendors, partners and 3rd parties, delivering an ‘extended enterprise’ capability
  • Infrastructure to support exploration and development future value propositions
  • Central point to support a distributed architecture for globally distributed enterprises
  • Supports deployment of data to enterprise users outside the enterprise network, e.g., on mobile devices.
  • Ensures all customers continually benefit from the Sabisu agile devops approach
  • Elastic compute resource to support rapid expansion and onboarding new customer

The cloud really works for us and has proven key to all those points.

So an SaaS deployment is entirely possible. In fact, it’s key to Sabisu, as it’s often the initial engagement with a new customer.

Architecture v2 - SaaS

Why keep the on-premise?

If the cloud is so great, why not offer Sabisu as a purely SaaS play?

Simply put, that’s not what the customer needs. Most of our customers have vast quantities of enterprise and manufacturing data in the enterprise and do not want to risk that data, either by shifting it to the cloud or by exposing it to our cloud via something like VPN.

All enterprise data is served directly from the on-premise within the enterprise network to a client. We guarantee enterprise network integrity.

Architecture v2 - Hybrid

The on-premise looks after authentication, integration and aggregation. It sits within the customer’s network so they can be confident that their data isn’t leaving their enterprise unless they specifically instruct that it should. Even then, only aggregated data is passed.

The on-premise is close to the source data so that aggregation, calculations and analytics can be fast. Sabisu doesn’t have to move large quantities of data to the cloud.

The on-premise is designed for deployment anywhere; middle of an enterprise network, edge of a distributed network with limited bandwidth, in the cloud to demarcate separate projects or organisations.

On-premise servers also have a trick up their sleeves; they can be federated to allow load balancing or inter-premise data exchange.

Architecture v2 - Distributed

Hybrid-cloud + Cloud-tethering = hybrid-SaaS

The cloud is a self-provisioning, resource pooled, possibly multi-tenant, elastic compute resource. So a hybrid-cloud is a combination of this with an on-premise component.

If the cloud’s elastic compute capability is needed it’s easy to utilise; spin up another server to scale, scale using services, aggregate a little less on-premise or load the bandwidth to the cloud a little more.

All that great cloud capability mentioned at the top of this post is in play, but with the added capabilities of the on-premise, particularly around integration.

The on-premise component could be highly virtualised itself – a virtual, private cloud, consisting of hundreds of on-premise servers. Or it could be a lone server. It doesn’t matter to the end user.

The on-premise and cloud is brought together seamlessly on the end-user’s machine. Therefore it would be more useful to call Sabisu a hybrid-SaaS platform; to the user there is no discernible gap between the cloud and on-premise experience.

Of course, some key capabilities such as external access, collaboration and distribution of data depend on that cloud capability as it acts as the conduit, effectively tethering the user to the cloud and on-premise servers to get the full range of Sabisu capabilities.

This gives the ability to implement new capabilities at zero cost, e.g., pushing data to plant mobile devices without any changes to IT infrastructure to gain substantial bottom line efficiency savings.

And that’s where tethering comes in; ensuring that users have on-demand access to product features as soon as they’re available so they can get that competitive edge.


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Sabisu’s Updated VBar

This week we’ve introduced a couple of big changes to the main Workplace in Sabisu, the first being the unified timeline and the second being some adjustments to the VBar to provide quicker access to core features like Notes & Actions.

We’ve reshuffled the VBar icons this week, re-organising them to improve visibility for the day-to-day items being used in Sabisu.

You’ll also see a new ‘+’ icon in the VBar which gives quick-access feature which will let you create a new Note or Action instantly or invite someone to one of your Communities.


Chat has also moved to the top of the VBar and received some visual improvements to your contacts and Community list.

If your one of our advanced users looking after Widgets or Publications you’ll now find the options at the bottom of the VBar. We’ve not changed how any of these behave – just where they are on the screen.

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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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