User-Friendly RASCI Accountability Matrices

Right now, you’re probably thinking that’s a statement of opposites. Something dreamed up by a consultant to impress, or just to fill a blog page. But wait. What if I taught you to create order in procedural chaos in five minutes flat? ?Would you be interested then?

The first step is to create a story line ?

Let’s imagine five friends decide to row a boat across a river to an island. Mary is in charge and responsible for steering in the right direction. John on the other hand is going to do the rowing, while Sue who once watched a rowing competition will be on hand to give advice. James will sit up front so he can tell Mary when they have arrived. Finally Kevin is going to have a snooze but wants James to wake him up just before they reach the island.

That’s kind of hard to follow, isn’t it ?

Let’s see if we can make some sense of it with a basic RASCI diagram ?

Responsibility Matrix: Rowing to the Island
Activity Responsible Accountable Supportive Consulted Informed
Person John Mary Sue James Kevin
Role Oarsman Captain Consultant Navigator Sleeper

?

Now let’s add a simple timeline ?

Responsibility Matrix: Rowing to the Island
? Sue John Mary James Kevin
Gives Direction ? ? A ? ?
Rows the Boat ? R ? ? ?
Provides Advice S ? ? ? ?
Announces Arrival ? ? A C ?
Surfaces From Sleep ? ? ? C I
Ties Boat to Tree ? ? A ? ?

?

Things are more complicated in reality ?

Quite correct. Although if I had jumped in at the detail end I might have lost you. Here?s a more serious example.

rasci

?

There?s absolutely no necessity for you so examine the diagram in any detail, other to note the method is even more valuable in large, corporate environments. This one is actually a RACI diagram because there are no supportive roles (which is the way the system was originally configured).

Other varieties you may come across include PACSI (perform, accountable, control, suggest, inform), and RACI-VS that adds verifier and signatory to the original mix. There are several more you can look at Wikipedia if you like.

Check our similar posts

Migrating from CRM to Big Data

Big data moved to centre stage from being just another fad, and is being punted as the latest cure-all for information woes. It may well be, although like all transitions there are pitfalls. Denizon decided to highlight the major ones in the hope of fostering better understanding of what is involved.

Accurate data and interpretation of it have become increasingly critical. Ideas Laboratory reports that 84% of managers regard understanding their clients and predicting market trends essential, with accelerating demand for data savvy people the inevitable result. However Inc 5000 thinks many of them may have little idea of where to start. We should apply the lessons learned from when we implemented CRM because the dynamics are similar.

Be More Results Oriented

Denizon believes the key is focusing on the results we expect from Big Data first. Only then is it appropriate to apply our minds to the technology. By working the other way round we may end up with less than optimum solutions. We should understand the differences between options before committing to a choice, because it is expensive to switch software platforms in midstream. data lakes, hadoop, nosql, and graph databases all have their places, provided the solution you buy is scalable.

Clean Up Data First

The golden rule is not to automate anything before you understand it. Know the origin of your data, and if this is not reliable clean it up before you automate it. Big Data projects fail when executives become so enthused by results that they forget to ask themselves, ?Does this make sense in terms of what I expected??

Beware First Impressions

Big Data is just that. Many bits of information aggregated into averages and summaries. It does not make recommendations. It only prompts questions and what-if?s. Overlooking the need for the analytics that must follow can have you blindly relying on algorithms while setting your business sense aside.

Hire the Best Brains

Big Data?s competitive advantage depends on what human minds make with the processed information it spits out. This means tracing and affording creative talent able to make the shift from reactive analytics to proactive interaction with the data, and the customer decisions behind it.

If this provides a d?j? vu moment then you are not alone. Every iteration of the software revolution has seen vendors selling while the fish were running, and buyers clamouring for the opportunity. Decide what you want out first, use clean data, beware first impressions and get your analytics right. Then you are on the way to migrating successfully from CRM to Big Data.

Contact Us

  • (+353)(0)1-443-3807 – IRL
  • (+44)(0)20-7193-9751 – UK
Using Pull Systems to Optimise Work Flows in Call Centres

When call centres emerged towards the end of the 20th century, they deserved their name ?the sweatshops of the nineties?. A new brand of low-paid workers crammed into tiny cubicles to interact with consumers who were still trying to understand the system. Supervisors followed ?scientific management? principles aimed at maximising call-agent activity. When there was sudden surge in incoming calls, systems and customer care fell over.

The flow is nowadays in the opposite direction. Systems borrowed from manufacturing like Kanban, Pull, and Levelling are in place enabling a more customer-oriented approach. In this short article, our focus is on Pull Systems. We discuss what are they, and how they can make modern call centres even better for both sets of stakeholders.

Pull Systems from a Manufacturing Perspective

Manufacturing has traditionally been push-based. Sums are done, demand predicted, raw materials ordered and the machines turned on. Manufacturers send out representatives to obtain orders and push out stock. If the sums turn out wrong inventories rise, and stock holding costs increase. The consumer is on the receiving end again and the accountant is irritable all day long.

Just-in-time thinking has evolved a pull-based approach to manufacturing. This limits inventories to anticipated demand in the time it takes to manufacture more, plus a cushion as a trigger. When the cushion is gone, demand-pull spurs the factory into action. This approach brings us closer to only making what we can sell. The consumer benefits from a lower price and the accountant smiles again.

Are Pull Systems Possible in Dual Call Centres

There are many comments in the public domain regarding the practicality of using lean pull systems to regulate call centre workflow. Critics point to the practical impossibility of limiting the number of incoming callers. They believe a call centre must answer all inbound calls within a target period, or lose its clients to the competition.

In this world-view customers are often the losers. At peak times, operators can seem keen to shrug them off with canned answers. When things are quiet, they languidly explain things to keep their occupancy levels high. But this is not the end of the discussion, because modern call centres do more than just take inbound calls.

Using the Pull System Approach in Dual Call Centres

Most call centre support-desks originally focused are handling technical queries on behalf of a number of clients. When these clients? customers called in, their staff used operator?s guides to help them answer specific queries. Financial models?determined staffing levels and the number of ?man-hours? available daily. Using a manufacturing analogy, they used a push-approach to decide the amount of effort they were going to put out, and that is where they planted their standard.

Since these early 1990 days, advanced telephony on the internet has empowered call centres to provide additional remote services in any country with these networks. They have added sales and marketing to their business models, and increased their revenue through commissions. They have control over activity levels in this part of their business. They have the power to decide how many calls they are going to make, and within reason when they are going to make them.

This dichotomy of being passive regarding incoming traffic on the one hand, and having active control over outgoing calls on the other, opens up the possibility of a partly pull-based lean approach to call centre operation. In this model, a switching mechanism moves dual trained operators between call centre duties and marketing activities, as required by the volume of call centre traffic, thus making a pull system viable in dual call centres.

Contact Us

  • (+353)(0)1-443-3807 – IRL
  • (+44)(0)20-7193-9751 – UK
What GDPR Means in Practice for Irish Business

The General Data Protection Regulation (GDPR) is a European directive aimed at ring-fencing consumer data against illegal or unnecessary access. There is nothing to discuss or debate with local politicians, or the Irish Data Protection Commissioner for that matter. As a European directive, it has over-riding power. To obtain an English version, please visit this link, and select ?EN? from the table of languages.

As you reach for your tea, coffee or Guinness after sighting it, you will be glad to know the Irish Data Protection Commissioner has the lead in turning this into business English we understand. The following diagram should assist you to obtain a quick overview of the process we all have to go through. In this article, we briefly describe what is inside Boxes 1 to 12. The regulation comes into force on 25 May 2018 so we have less than a year to get ready.

The 12 Essential Steps to Implementing the General Data Protection Act

1. Create awareness among your people of what is coming their way. The GDPR has given our regulator discretion to dish out fines up to ?20,000,000 (or 4% of total annual global turnover, whichever is greater) so there is determination to make this happen.

2. Become accountable by understanding the consumer data you hold. Why are you retaining it, how did you obtain it, and why did you originally collect it. Now you know it is there, how much longer will you still need it? How secure is it in your hands, have you ever shared it?

3. Open a communication channel with your staff, your customers, and anyone else using the data. Share how you feel about how accountable you have been with the information in the past. Explain how you plan to comply with the GDPR in future, and what needs to change.

4. Understand the personal privacy entitlement of the subjects of the information. They have rights to access it, correct mistakes, remove information, restrict its use, decline direct marketing, and copy it to their own files. What needs to change in your systems to assure these rights?

5. Issue a policy for allowing consumers access to their information you hold. You must process requests within a month, and you may not charge for the service unless your cost is excessive. You may decline unfounded or excessive demands within your policy guidelines.

6. Adapt to the requirement that you must have a legal basis for everything you do with, and to consumer data. You need to be in a position to justify your actions to the Irish Data Protection Commissioner in the event of a complaint. Having a legitimate interest is no longer sufficient.

7. Ensure that consumer consent to collect, use, and distribute their data is ?freely given, specific, informed, and unambiguous.? From 25 May 2018 onward, this consent will be your only ground to do so. You cannot force consent. Your benchmark becomes what the GDPR says.

8. Issue rules for managing data of underage subjects. This is currently under review and we are awaiting results. Put systems in place to verify age. Set triggers for where guardians must give consent. Make sure age is verifiable. Use language young people understand.

9. Introduce a culture of openness and honesty, whereby breaches of the GDPR are detected, reported, investigated, and resolved. You will have a duty to file a GDPR report with the Data Protection Commissioner within 72 hours, thus it is important to fast track the process.

10. Introduce a policy of conducting a privacy assessment before taking new initiatives. The GDPR calls for ?privacy by deign?, and we need to engineer it in. This may be the right time to appoint a data controller in your company, and start implementing the GDPR while you have time.

11. You may also need to appoint a data protection officer depending on the size of your business. Alternatively, you need to add managing data protection compliance to an employee?s duties, or appoint an external data-protection compliance consultant.

12. Finally, and you will be glad to know this is the end of the list, the GDPR has an international flavour in that multinational organisations will report into the EU Lead Supervisory Authority. This will manage the process centrally while consulting national data authorities.

The GDPR is a project we all need to complete. If we are out of line, it is in our interests to get things straightened out. Once everything is in place, the task should not be too onerous. Getting there could be the pain.

Contact Us

  • (+353)(0)1-443-3807 – IRL
  • (+44)(0)20-7193-9751 – UK

Ready to work with Denizon?

Contact Us Today