Agile organisations VALUE these metrics MORE than traditional ones to drive its culture of agility and high-performing teams. The key here is to notice that traditional metrics are strengthened, not replaced by agile ones.
Agile methods are not lighter and should not be seen through the traditional lens!
We have published previous articles on the current reality of BI project challenges in the African context. Let’s do a quick recap on our 2015 findings to date on Business Intelligence Technology Selection – 5 Crucial points for IT and business Executives are listed. The aim of this article is to serve the project teams at our clients with some key learning points for starting off with BI technology selection. The goal is to promote time savings for BI selection and implementation and to speed up the process to provide the organization’s access to trusted, balanced and centralized information. This article does not advise our audience on any specific technologies or how to go about the selection itself.
1. In mind – project management challenges: Scope, Cost and Time. These three, hand-in-hand, are still the biggest competitors to quick BI value realization. Project Resourcing is now less of a problem for the generally implemented technologies in the African space. The technology you select, within the context of your organisational domain and architectural landscape, will largely determine the significance of these challenges during implementation. The reasons for these three elements can be further expanded upon in the next four.
2. Design as consideration during selection: Architecture and Integration challenges are the main contributors to the extension of scope and time in major BI implementations. A key learning to take into your project from the failures of others, regardless the technology for your core BI solution, is to ensure that the exact amount of integration required in your application landscape as well as how that couples with current architectural standards such as centralized master data management(MDM) and other components in a specific architectural model (eg. Service Oriented Architecture (SOA)) is scoped out well enough even before the selection and blueprinting is done. Having a clearly defined view of your integration scope and effort before entering the selection and design phases leads to less changes and scope creep during implementation.
3. Broader architectural change: The landscape for BI implementations have changed from an architectural perspective. The average large corporate now spends 30% of their annual IT budget on landscape redesign, optimization and revitalization to keep in line with sudden strategic business changes and unforeseen economic pressures leading to corporate restructuring, relocation, mergers and acquisitions etc. The consequence for BI is that it tends to play catch-up now more than before. There is no simple solution be it through governance or standard management practices to ensure this causes less delays on BI projects. The answer resides in having a truly scalable, yet loosely coupled BI design framework right at the core where the integration between data components become your reality. We strongly recommend that you do not engage any specific technology until your project team understands your architectural principles and challenges. Ensure that your design is ready for rapid change. The entire structure from base data through staging to cubes now, more than ever before, requires a flexible, ‘plugin-plugout’ design. More than a stock standard Kimble or Inman design is required to ensure that flexibility and scalability becomes an enabler to BI for sudden major landscape changes.
4. The belief that BI can ‘quickly become “real-time” and “in-memory”’. These words have become like the silver bullets, yet ever still not piercing the layers of IT kevlar compounding its challenge. Yes, there are numerous solutions to enforce the possibility of this belief. However, big-data still bares its constraints, not only by Moore’s law, but also by solid architectural common sense. Where data volume meets constraints which are uncontrolled by your chosen BI technology (eg. Large batch loads, flat file processing, delayed message queues, network lag and infrastructure issues, legacy integration), real-time is always an actual idea but still a very idealistic concept. The African context does not yet completely lend itself to real-time BI as much as the major BI technologies at hand may enable real-time and in-memory analytical capabilities by themselves. Senior Executives need to consider the constraints in their IT landscape before choosing to invest the ‘major bucks’ on the latest, most niche BI technology promising to deliver cross-organisational real-time performance views and dashboards.
5. Executive support: Mainly for reasons 2 and 4 above, executives are often frustrated with the amount of time and money invested to deliver singular, static reporting which could effectively be delivered with much more rapid and less costly approaches. Focus on providing your stakeholders with a proper short- to medium term VS long term BI strategy before engaging on a suitable technology investment, regardless the ‘bells and whistles’ on offer.
Context
One of the goals of master data management (MDM) is to maintain a single access point to specified data sets, thereby creating a single view of information. Whether customers, products, or other entities, organisations may struggle to measure the actual benefits of consolidating and centralising data to have a single viewpoint of data.
Beyond the ability to use a centralised access point to gain a better perspective of what is going on in the organisation, organisations are always required to provide an account of the financial benefits of new IT initiatives. Within MDM, business intelligence (BI), business process management (BPM), etc. the immediate return on investment is not always seen. Unfortunately, some organisations choose not to look beyond immediate financial gains when looking to implement new solutions or change the way they currently do business.
The management of master data touches more than just the information that is maintained. By creating data repositories that reflect business functions, organisations can develop views of data that give users access to the information required to do their jobs effectively and efficiently.
To better understand the business benefits of MDM, it is important to recognise an organisation’s general information infrastructure and how silos of data have been formed within organisations creating a splintered view of what is occurring within the organisation. Then it is possible to understand the benefits of the creation of a centralised repository of data – regarding both the information accessed and the business benefits that go beyond data.
Many business executives have come to realise that with large distributed ERP and CRM systems, MDM is not only pivotal to operational success, but also to delivering business insight. It enables business intelligence and reporting solutions with accurate hierarchical and trusted master data. Without it, business intelligence is often lost for numbers.
So, what qualifies the investment in an MDM solution if financial justification doesn’t add up. It’s simple – an MDM implementation is risk-motivated. I bought my first motorcycle knowing that it cost more than the savings on fuel. Buying and riding in itself implied risk, but what would the time of commuting by car in South African city life cost me in the long run if I didn’t? Yet you might still ask, what does this have to do with motorcycling?
Fractured Information Views
Due to the nature and development of information architecture and the development of enterprise information systems, the general structure of data is that it exists in silos across the organisation. Before the concept of centralised data stores, many systems were developed to meet a business function without taking into account the big picture. Consequently, with the addition of new systems, data storage volumes and the number of transactions grow exponentially. This means that in many organisations several disparate systems exist that contain similar information but that don’t interact with other relevant systems within the organisation. In addition to this lack of interaction, duplicate data may be processed in different systems, creating duplicate work for end users and different data structures. This adds to the difficulty of looking for and identifying like data across the organisation.
The bottom line is that each system only gives a fractured view of what is occurring within the organisation. For instance, a customer relationship management (CRM) system may not have all of the account information of their customers from buying habits and payment histories, to product preferences. Different bits of information that reside in disparate systems, when brought together, create a full view of the customer. Add to this data quality efforts to standardise data views and to create a consistent view of data across the organisation and the beginnings of MDM are created.
Although MDM solutions give organisations central access points and a 360-degree view of data and entities, actual ROI is not always easy to quantify. After all, quantifying better customer service or identifying a decrease in product cycle times and tying that to an MDM initiative may not be intuitive for your organisation.
In this case, it’s like telling my wife to buy herself a Kawasaki because it’s so much faster around in traffic – problem is, she’s in insurance and from that perspective the numbers don’t add up despite the obvious benefits and highly necessitated requirement to get around town quicker.
MDM Benefits Explored
There are many benefits for organisations choosing to implement an MDM solution that result from creating a consolidated view of data. To identify the ROI associated with MDM, some key benefits should be highlighted that go beyond the consolidation of data towards increasing efficiencies within organisations and lowering the cost of doing business.
The most obvious benefit of customer data integration (CDI)/MDM solutions is the creation of a single customer view. By creating a centralised access point of contact to customer, product, or other forms of data, not only are organisations able to gain a better overall view of business entities and what is occurring with accounts, but also data quality efforts become consistent. For organisations trying to attain one view of entities, the ability to ensure data quality throughout the organisation means that consistency of business rules, data cleansing and the standardisation of business processes will become an ingrained process within the overall environment.
Despite the fact that there is a constant focus on business pains and a push to focus on business rather than data, an organisation’s healthy business environment depends upon the data that supports it. Therefore, having strong data quality efforts in place helps organisations maintain healthy information systems, identify how systems across the organisation interconnect and enable better business process management. Although IT benefits are important, business benefits are also relevant. Using customer data as an example, end users can provide better service quicker thereby increasing customer retention and satisfaction.
Because of compliance and the need to maintain specific data sets and output on a regular basis, MDM enables organisations to manage the processes essential to meet compliance requirements. This in turn translates into the ability to plan and budget more efficiently. With a broader understanding of what is occurring within the organisation, financial processes can offer a more complete view of the organisation.
Finally, building an MDM solution enables organisations to develop and maintain a strong data governance program. Because all of the mechanisms have been put in place, data governance becomes a natural extension of an MDM implementation.
Once my wife bought her first Kawasaki, we had to ensure she bought all the gear too – in pink.
Focusing on Value
Whether by focusing on business or IT, the values of MDM are numerous. Actual implementation of an MDM solution is process and resource intensive as well as continual based on the need for constant data quality and business process improvements. However, these efforts enable organisations to have a synchronised and 360-degree view of the data that helps drive their business success.
So, these days we buy the best fuel for our motorcycles. You’ve probably figured out by now that the fuel is like data… the cleaner, the better the engine runs.
Conclusion
Not having MDM in an organisation with duplicated, heterogenous sources of information in expensive enterprise resource planning and customer resource management systems is like buying an Aprillia MV but running out of clean fuel in the long run. Having spent all that money on an efficient engine but not being willing to invest in running cleaner fuel might just result into no ride at all despite the seemingly financial hard ride in the short term..
-Jacob George
Solutions Architect
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