| It is often assumed that a data warehouse is | | | | always be aware of what's being used and what |
| imperative to any business, normally because IT | | | | isn't, if it's not being used you need to know why, |
| departments don't want to report directly off | | | | if it's simply a case of staff not having the time |
| their source systems and they decide they need | | | | or understanding it, you need to invest the |
| a data warehouse. | | | | resources to rectify this, or if it's genuinely not |
| Building a data warehouse can be a large and | | | | working you'll need to find an alternative solution. |
| costly project so you need to consider what real | | | | Once your users see this technology can make |
| benefits it could bring to your business, you need | | | | their job easier, you'll start to see the value in |
| to think carefully about why you're doing it and | | | | your investment. |
| what the purpose is. | | | | This always works from the opposite perspective |
| Maybe you can report directly off your source | | | | too, if one of your users are told any requests |
| system by improving the capacity of that server | | | | will take several weeks or months to action, they |
| or that hardware you're using. By duplicating and | | | | will soon stop asking and the technology wont be |
| just reporting off it, you'll very quickly get | | | | being used to it's full potential. |
| management information out to people and it | | | | Data quality will always cause risk because you're |
| might be adequate, further more, by doing this | | | | pulling data from several systems and from |
| you'll learn your data, and understand the pitfalls in | | | | several years and this means the chances are the |
| it. | | | | data has been captured in different ways. |
| This means that you've already solved a lot of | | | | There are several steps you can take to ensure |
| the issues and problems if you do decide to build | | | | the risk is as minimal as possible starting with |
| a data warehouse in the future, so when you out | | | | exposing the data to the end user as soon as |
| grow reporting directly off your operation | | | | possible because it's their data and it's them who |
| systems you can then evolve quite easily | | | | are going to be able to resolve whether the data |
| because you now have more knowledge into | | | | is right or wrong. |
| building a data warehouse. | | | | There are plenty of data quality tools that can |
| One of the biggest challenges you're likely to face | | | | help when it comes to automating and cleansing |
| with business intelligence when it comes to | | | | the data that's been gathered. These tools can |
| ensuring you merge prospective customers is | | | | help merge and de-dupe the data and they can |
| making sure your data is merged and collated to | | | | use fuzzy look up logic and look up to catalogues. |
| give you the right information at the right time. | | | | If you have units of measurement across your |
| Your customer relationship management (CRM) | | | | product range, these units can be standardized |
| system may know who your customers are, but | | | | and converted to clean your data automatically. |
| it might not know when one of your customers | | | | The main challenge here is making sure all the |
| transacts with you. | | | | data is standardized and documented as soon as |
| This can become even more problematic if you | | | | possible. The different business rules are |
| have several transactional systems and channels | | | | documented and shared and this all falls under the |
| to market. For example, if you were to take a | | | | umbrella of a data governance programme to be |
| football club that may have it's season ticket | | | | managed, coordinated, and governed. |
| holders, then turn style ticket holders and a shop. | | | | In most companies it will be the finance |
| You may not know that a customer brought | | | | department that looks after the planning and |
| something from the shop even though they've | | | | budgeting using Excel, meaning it's done outside |
| got a season ticket, so you'd use a data | | | | the core IT systems. The planning and budgeting |
| warehouse to pull all that data together giving you | | | | is a key part of your information management |
| a single view of the customer that can be sent to | | | | through monthly or quarterly forecasting which is |
| the CRM solution and the marketing campaign can | | | | often feed into the management information |
| be targeted accordingly. | | | | systems. |
| The problem often lies in ensuring the CRM | | | | These two systems ideally should both be |
| doesn't already have that information and | | | | incorporated into the business intelligence solution |
| duplicating it, as most CRM systems will contain | | | | because it's a key input. This means the data |
| duplicate data anyway (with different titles, | | | | warehousing, business intelligence reporting |
| names, addresses etc), you need to ensure the | | | | process, month end reporting and planning and |
| tools you are using can integrate that data | | | | budgeting are actually interlinked and as such you |
| effectively from the data warehouse into the | | | | need to structure your programme and you |
| CRM solution and vise versa as the data comes | | | | business intelligence deployments taking them into |
| back. | | | | account. |
| Another major obstacle your business may | | | | When we're doing data warehousing and pulling |
| encounter when implementing a business | | | | data from many sources, you often have the |
| intelligence solution is user adoption - getting your | | | | same data coming from multiple systems causing |
| staff to actively analyse the data you're producing | | | | issues with duplication and inconsistencies. The |
| and using the data gathered. | | | | data quality, customer data or product data often |
| Just because the IT department has brought | | | | has to be merged into a master set, often the |
| some new tools, doesn't mean the rest of your | | | | data warehouse becomes the master data |
| staff are going to use them, they might not have | | | | management product. It's often ideal if you can |
| the time, they might not see the benefit to them | | | | manage you master data, and put it in as part of |
| or they simply may not understand it. This means | | | | your data governance programme early on in the |
| as part of implementing a new BI solution, you | | | | project then that will help your BI deployment |
| need to understand your users and deliver the | | | | later on. This means the your master data will be |
| information to them in a way that's going to | | | | known, you'll know who controls it and the |
| make them want to use it too. This needs to be | | | | procedures and principles around managing it. |
| looked at on an ongoing basis, you need to | | | | |