But not only the data structure versus the setup of the data warehouse constitutes a problem. Big Data also challenges data warehouses as to the speed with which data becomes available, which is many times greater than data warehouses were supposed to handle. The question that therefore arises is: “Will data warehouse survive the Big Data challenges?”

Big Data
The business case for the data warehouse is that it acts as a ‘’single version of the truth”. A single logical store collecting and reconciling data from multiple systems, providing information and results in a unified manner to the business. An almost infinite source to extract valuable business insights from. In practice, however, as the volumes of data increases, the data warehouse becomes “obese”.  Data comes in at regular times, with the ‘exercise’ done on the data warehouse limited almost exclusively to standard reporting and some analysis. Perhaps even more important, with the rise of Big Data, data is coming faster and in greater volumes, while there is a smaller time window to collect, combine, and transform this into actionable information.

Organizations need to understand that Big Data is not only about bigger quantities of data, it’s also the fact that it’s mostly unstructured. Data warehouses are built on relational data models that are very well suited to support transactional systems and the structured data used. An important part of Big Data however is unstructured (or semi-structured) data -for example audio, video and e-mails-. Not something the data warehouse was ever expected or designed to handle.

Big data giving (re)birth to data warehouse
For now it seems that the data warehouse is still here to stay, representing the most cost effective way to streamline and unify structured data in support of business needs. Organizations with a data warehouse wanting to make use of Big Data currently have two options: combining or fusing Big Data with their data warehouse. By combining traditional data warehouses with emerging Big Data technologies organizations can gain insights from both structured and unstructured data. Another option is to fuse Big Data technologies into existing data warehouse products. An example of this approach is Microsoft PDW (Parallel Data Warehouse) appliance which has Hadoop-technology embedded.

With Big Data requiring new approaches and tools to deal with this overwhelming amount of unstructured data in an efficient manner, the death of data warehousing is far from reality. In fact, data warehouses are combining and/or fusing with Big Data technologies in order to take advantage of Big Data. Business value coming from Big Data is derived from advanced analytics based on the combination of both traditional enterprise data and new data sources. With the advent of Big Data, it seems like Big Data is giving rebirth to the data warehouse.

Below are a few of the best articles we’ve seen published this year.

Top 10 Challenges for Investment Banks 2012

To help investment banks plan and execute with success as macro trends reshape the industry, Accenture has developed a list of the top 10 challenges to address in 2012.

Different key ingredients for successful Innovation

The responses to a poll Accenture ran on LinkedIn revealed that people have very different ideas about the key ingredients for successful innovation. A full half of the respondents believe that understanding customer challenges is the key, while another third believe it is essential to embed innovation in an organisation.

The truth is that all of these play a role in successful innovation. But is there one element that is so crucial that without it successful innovation would be impossible?

The importance of Data Governance
Does your company consider its data to be a strategic asset? More important, do you not only consider it to be an asset, but are you also treating it as any other asset?

In my 20 years of experience in Data and Information Management, I worked for companies, who claimed to understand the importance and potential value of their data, but struggled to live up to this understanding. And, up to today, many companies I visit are still not taking full benefit of the wealth of information that is stored in their systems.

Are you moving fast enough in Digital?
As the world is changing rapidly, organizations need to stay in the game by investing in digital capabilities. But many organizations find it difficult to predict the expected ROI, resulting in little investments and lack of innovation.

Digital is radically changing the traditional ways organizations interact with customers. Customers are willing to interact with organizations via social media, and expecting them to be out there too! Therefore the question is no longer how fast things are moving, but: are you moving fast enough?

High Performing Business in changing economic times
In this volatile, uncertain and increasingly complex world, many businesses and governments have an urgent need to reassess current strategies and vital capabilities to achieve high performance.

Companies that want to stay in business need to make concrete plans for the future, with little room for errors. After all, companies will need to keep launching successful business concepts or products to outperform their rivals in these challenging times.

What I am recently asking myself, is how this traditional approach of data management will change as a result of the digital revolution we are currently going through. When I am hosting a workshop or giving a presentation I always like to show a movie that tells the story of the enormous explosion of data and information in recent years and how most organizations feel they are not in control of this.

In a relatively short time the concept of data is changing dramatically. Not just in volumes, which become bigger and bigger, but also in the number and type of sources this data is coming from. In todays world, most machines, tools, cars, websites, buildings etc.  store and even transmit data. And, to add to the complexity, this data is not by definition formatted in a way most of us are used to. In summary, we face higher volumes of data, coming from a higher number of sources both in a structured and unstructured way.

My personal Big Data Watch
Thinking of this and trying to understand the impact of it, I realized this is also affecting me in my personal life. I am a passionate runner, and, as can be expected from a data geek, I keep logs of my runs. It is even less than 10 years ago, when I had a watch with a stopwatch and I was able to measure the time of my run. This I would then carefully register in an Excel sheet and with some clever formulas I was able to make nice overviews of my weekly running time. I could also compare this to my training schedule I always made when I was preparing for a marathon.

Then, I think it was somewhere around 2006, I bought a fantastic watch with GPS. Now I was also able to register the distance of my run. Also, I could now even see on a map where I was running and save the route if I felt the run was worthwhile to do again. The watch even had built in programs against which I could run and it would keep track of where I was. For example if I scheduled a run of about 1 hour against 12K/h, there would be a little guy on the display of my watch running at that speed and another little guy (me) would be in front or after this guy, depending on my speed. Of course I still captured all my runs in the updated excel sheet (updated with distance) and was still able to do all the reporting I did before, but now with some new dimensions.

Unfortunately, the watch at a certain moment died. I immediately bought a new version, which, of course, had new and improved functionality. It was, first of all, a lot smaller and looked like a normal watch, but still had many of the same great features of the old one. Further, it also came with a USB connection, with which I could now transfer the data of my runs to a website and share it with other users of the watch. The website even helps me to find people living close to me and see their runs and e offers many, many features on reporting, keeps track of distance, timing, calories etc.

What does this mean for Data Management?
So, during a small period of time I went from a watch that kept time and which I stored in a traditional way on my laptop, to a situation in which I have a watch that tracks all kinds of metrics, which are then stored and shared on an external website. And, on top of that, I now have 24/7 access to my data, not just with my watch, but also with my IPhone, IPad, Samsung Galaxy, laptop, my wife’s laptop, my wife’s IPhone , basically with any machine that has an internet connection.

If I try to translate this to my professional life as data management geek, and focus on the data governance part, my initial conclusion is that, in the end, I am still doing the same things I was doing before:

-          In my role as data owner I still decide which data I want to use to support my running.

-          In my role as data sponsor, I have freed up the budget to buy the right watch, that captured the data that I, as data owner, needed to manage my running performance.

-          In my role as data steward I have done a comparison between the different options offered by different watches and finally choose the one that best fitted my budget and requirements.

-          And, as data steward I am still maintaining the data and determining the standards for the, for me, relevant parts of the data that are captured.

Based on this very simple example, I might conclude that, from a data governance viewpoint, Big Data is not bringing that much news. The basics for data management in the end do not seem to change. My most important observation is that with the rise of Big Data, data management even becomes more important to allow organizations to focus on those sources, types and volumes of data that bring most value for their business.

Open Source Conference 2012.

On December 14th Accenture and Redhat are organizing the 5th edition of the Open Source Conference, the largest conference on Enterprise Open Source in the Benelux. Discover the latest trends and innovations in the field of Enterprise Open Source, Big Data, Cloud Computing, Social Media and Mobile Technologies. 

For more information of this free event, please visit the OSC 2012 site: http://bit.ly/SnvQjN.

As data-driven insights become an increasingly critical competitive differentiator, companies will use them to drive and optimize business decisions across industries. In the past, this market was largely limited to traditional market research and data specialists, but with the rise of digitization, established enterprises have amassed terabytes of information about their customers enabling them to potentially become serious players in the new information game.

The benefits of Big data and next-generation Analytics
There is a need for not just storing but also accessing and analyzing data across an enterprise to make business sense of it. It is only when we combine sources of data (both within and outside an organization’s firewall) with analytics applications that we can make information meaningful within a business context. Therefore Big data presents new ways to reverse the slide and boost profitability. The benefits of big data and next-generation analytics are more expansive than improved customer relationship management. This digital treasure trove, already highly valued as a way to help meet the evolving needs of customers and spot trends, can help companies create new products and services, and perhaps even spawn entirely new businesses. Moreover, a significant number of consumer-facing organizations have natural advantages in this area; for them, leveraging Big Data represents a particularly lucrative opportunity.

In particular, mobile carriers are in a prime position. Today’s customers have a endless supply of information at their fingertips. Smartphones, for example, enable much faster access to brand, product and price-comparison information. As a result, companies in multiple industries are having difficulty attracting and retaining customers. The big problem for mobile carriers is the vast amount of detail about new behaviors is largely opaque to them. Visibility into customer behavior is declining as the data sphere eclipses the voice sphere. With voice usage, they could track call length, who’s calling whom and more, but the data sphere remains unknown terrain.

360 degree view
Information services evolve at great speed, and the winners typically are those that are fastest to market. As Google and Facebook have proven, shareholders place a high value on information companies. Rather than remaining as infrastructure companies or utilities, a mobile operator’s strategic plan might include ways to monetize information assets. Leading operators will be thinking broadly about new products and services they might provide. As data assets are transformed into new revenue streams, next-generation analytics will become integral to high performance.

At Accenture, we consider big data and analytics as the next frontier of technology evolution. Crucially, instead of basing major business decisions on intuition, corporations need to mine the data and information at their disposal. To dominate the ecosystem, corporations must act quickly or lose out to aggressive competitors, whether they are familiar industry players or innovative companies from other industries. More data means more opportunities for operators to gain insight about customers, new ways to serve customers, and to offer well-tailored products and services. The most innovative corporations will begin now to find ways to turn Big Data into big assets. With a 360-degree view of customers, they are able to better understand and more effectively engage with customers to win their hearts and minds.

Open Source Conference 2012.

On December 14th Accenture and Redhat are organizing the 5th edition of the Open Source Conference, the largest conference on Enterprise Open Source in the Benelux. Discover the latest trends and innovations in the field of Enterprise Open Source, Big Data, Cloud Computing, Social Media and Mobile Technologies. 

For more information of this free event, please visit the OSC 2012 site: http://bit.ly/SnvQjN.

Social media has fundamentally changed the way businesses and individuals connect and interact with each other. It offers individuals the opportunity to express their opinions and feelings at any given time. Such messages may be positive, but may also have a negative scope. Therefore organizations must be alert about what is said about their organization, products and services (and staff) in order to respond quickly. Protecting the organizational reputation (reputation management) is therefore one of the many possible applications of social media analytics.

In a broader perspective social media can be seen as a tool to understand what stakeholders are talking about (topic), what they think (opinion) and what they need (desire). It is not an additional channel for advertising products and services. Therefore it is wise to listen rather than talk and participate and facilitate rather than organize. Organizations that are making good use of social media have a direct line to what matters to stakeholders. Ignoring social media means that you are in risk of reputational damage and might miss identifying trends, or identifying trends later than competitors who are embracing social media.

Analytics
It is not strange then that analytics is important to many organizations. Analytics enables organizations to understand where the one remaining Dollar or Euro can best be spent on in order to get the maximum results. The growing amount of data (big data) is also one of the reasons why organizations show interest in analytics. An important contributor to big data is social media, where according to the latest figures about 1 billion people are involved in. Research shows that by using social media analytics the effectiveness of marketing can be improved by 52 percent and customer satisfaction by 43 percent. On marketing costs savings can be realized up to 38 percent and on support costs up to 32 percent.

A combination of social media data and CRM data enables organizations to have a more holistic picture of (potential) customers. CRM data consists of mainly structured data and relates to current and former customers. Social media data is for the most part unstructured and can relate to anyone. In order to understand the customer (voice of the customer) it is important to analyze both information sources, information obtained from customer services and surveys and information from social media. In this case, social media analytics is part of customer analytics and therefore it’s wise to perform category analysis, sentiment analysis and generate early warning and alerts. Although tools play an important role, ultimately the organization itself is at least as important.

Social media is not a choice anymore. Organizations are mentioned on social media (if they like it or not), and therefore it is better to know what is exactly being said about your company so you can act on it and engage. Social media in particular is a ‘listening’ medium in which analysis can provide additional insights that can benefit organizations. In addition, combining social media data with CRM data gives organizations a richer picture of customers and the ability to effectively and efficiently reflect the actual needs of customers. Organizations that ignore social media are not only missing opportunities but also expose themselves to unnecessary risks.

1. “Analytics is just another word for Business Intelligence’.”
   Business Intelligence is about understanding which were the relevant factors that lead to turnover and profit for example. Analytics is forward looking and provides insights on what results you can expect when choosing different scenarios. Of course, if you think that driving a car by looking only in the rear view mirror and not also looking through your windshield provides the same kind of experience and direction, then you probably would conclude that Analytics and Business Intelligence are the same thing.
2. “Analytics is a hype. In a few years nobody is talking about it anymore.”
   Absolutely! Why do you want to look ahead? The world is not dynamic and unpredictable. If you want to understand what lies ahead, just look at past results! There is no such thing as new developments: the world has not changed in the past twenty years.
3. “Analytics sure – but it’s not relevant for my business.”
   If your business has not changed in the past decades, you’re quite right. Today’s consumers are not active on the internet, comparing prices and leaving their opinions behind on your organization and products. And Social Media (another hype!) doesn’t provide the opportunity to understand what are the most important buying factors (price, availability, service?).
4. “I really want to embrace analytics, but not just now.”
   Let others overcome the hurdles of analytics. No need to experience all the bumps yourself. No risk that you will fall behind in understanding how make Analytics work for your organization.
5. “I want to embrace analytics, but my data is just not good enough.
   This is along the lines of: ‘My house is on fire, but my phone isn’t working so I can’t call the fire brigade’. You’re just unfortunate that you are the only organization with bad data…Cleaning data will probably take you a couple of decennia. Too bad it is also not possible to start with pockets of data.
6. “Analytics is for large organizations only, because it’s too expensive.”
   Small and medium organizations don’t do analytics. There are no organizations like Kaggle where top analysts take on your questions against almost no money. And yes, you do need very expensive software and even more expensive consultants to get cracking with Analytics.
7. “Analytics is a choice.”
   Here it is recognized that analytics actually exists and is something new – but it is not seen as a necessity. If market conditions or consumer preferences change – you can find out later. Absolutely ok – and no consequences.
8. “Analytics is the same as statistics and data mining.”
   This is the combination of: it is specific, expensive, scary and in the case of data mining it is also an empty promise.  To be short: you have to be stupid to give it any attention. Who needs a bunch of nerds who only talk in algorithms? Life is difficult enough.
9. “We will embrace analytics when the recession is over. There is really no budget – we must reduce costs! “
   A great case of calling the doctor after the patient has died. Some organizations think that when revenues, profit and budget are under pressure it is important to understand where to spend the one dollar or Euro you still have to get maximum results. Fortunately you’re not one of those companies! .

Considering these reasons, it is remarkable that in sports, where performance is everything, and everything is in service of the final result, analytics play a key role. Of course this does not apply to businesses, because performance is only an afterthought… right?

Do you have a tenth reason?

Based on the DELTA method by Thomas Davenport, Jeanne Harris and Robert Morison the Survey consists of 21 questions and tries to get answers to several important questions, such as “Which analytical characteristic are often present and shared within an organization?”

Which analytical characteristic are often present and shared within an organization?
The results indicate that organizations scoring high on analytics, do not do this in all fields of analytics. High scores on an area varies with lower scores in other areas. Further examination shows that data and data structure appear to be less important than people, goals, leadership and the organizational focus on analytics. Another notable factor is leadership, which shows that active support of analytics within an organization is clearly more effective than the actual use of analytics by these leaders.

The practice of analytics is quite different for each department. Analytics within the Finance department is the most accepted and embedded. A close second is the Sales & Marketing Department. Therefore the results indicate that analytics can be applied best to primary business components such as Finance and Marketing, and adds less value to facilitating business components such as Supply Chain & Operations and IT. Furthermore, the survey shows that analytics is less functional when the organizational focus is on efficiency and even less in the design and use of new work methods. With regard to design and new work methods, the result is easy to explain because such initiatives need more facilitative factors than analytics alone. It seems that analytics is well-suited for increasing the available information in an organization, and improves the flexibility of using information in general. This underlines the conclusion that analytics does not belongs with IT departments but must be used for the improvement of primary business.

Decisions based on “Gut feeling”
Senior employees have a more positive attitude towards analytics than junior employees. Given the higher score of Dutch directors for active support of analytics within the organization than for the actual use, it is possible that, although many organizations have invested in analytical capabilities, executives do not fully utilize their investment or even base their decisions on gut feeling. Although there is nothing wrong with using intuition and experience to aid decision making, these feelings need to be supported by data analysis. Informed executives make more insightful decisions that deliver improved business outcomes across the enterprise.

From strategy to execution, every organization regardless of its current capability can benefit by becoming more analytical over time.

So, you’re up and running now, but how do you make sure you keep running in the right direction?
Setting up a governance organization, implementing the right processes and supporting tooling is only the beginning. With delivering this, you made an important first step, but hopefully you also realize that data governance is here to stay. Successful companies that take data seriously have realized that really treating data as an asset requires a fundamental shift in thinking throughout the organization. In this posting I will briefly discuss several topics you need to address to keep your data governance initiative ‘running in the right direction’:

Communication
Ongoing communication is important for a number of reasons. First of all, you need to reach out to your business stakeholders, the data owners. Not only in order to stay on top of their business needs, but also to keep them involved and updated on the quality of their data. Appointing formal data ownership often turns out to be somewhat of a hollow shell, where someone is appointed and maybe initially takes his role seriously. Gradually attention will drop as day to day business asks for attention and the sometimes difficult topic of data will slowly drop off the agenda. My experience has learned that you have to be pro-active in keeping them engaged and involved. An important element here is to avoid ‘data-speak’ as much as possible and ‘talk business’.

Discipline
One of my favorite topics in establishing an ongoing successful data governance structure is discipline. Most of the dedicated data people I have met over the years (including myself) tend to solve a defect or issue as quick as possible if they see one. However, if you want to reach a certain level of maturity in the area of data management you should focus on providing feedback and education to your colleagues. Instead of solving issues, you have to reach out and explain what went wrong. If you only correct and solve defects you are covering up, not just the defects caused by others, but more importantly, your own added value.
Discipline also applies to having people follow processes. A lot of data defects are created in situations where people are not following the agreed upon processes. Therefore necessary checks are not being performed or people are put under pressure to quickly enter data in a system to meet deadlines. To ensure the quality of entering and maintaining data, make sure you encourage people to follow the proper processes and take out all creative shortcuts that have grown over the years into these processes. And, of course, set the example yourself.

Patience
Another key skill I found very useful in establishing a data management organization is patience. Patience, to explain and keep explaining among others the importance of data, why you have data standards and why following processes is important. These and many other topics need to be explained, and as you probably also have experienced, need to be explained time and time again. Keep on explaining, using business language instead of ‘data-speak’ as much as possible. It will help to create awareness and understanding.

The topics discussed do not provide the complete overview of activities and behavior. These are a few examples of activities and behavior, that in my own experience, have been important elements in keeping data management initiatives alive and relevant. That means there is much more you need to think of to make your data governance initiative an ongoing success. For now let’s conclude that sustaining your data governance initiative means hard work and dedication, not just from yourself, but from all layers in the organization.

Would be great to hear some of your experiences in sustaining data governance….