The 7 Questions Interview Series: Data and Supply Chain Data Integration
The 7 Questions Interview Series: Data and Supply Chain Data Integration “The 7 Question Series” is an investigative content series where we seek out key leaders in a specific industry and/or subject matter expertise area and ask them 7 key questions that “enquiring minds want to know”. There is a twist however to these questions. We provide the person being interviewed with a hypothesis for each question. This helps to frame and set context for their answer.
Data and Supply Chain Data Integration Series Objectives:
Data and Supply Chain Data Integration Series Objectives: The objective of this series is to establish direct connections with data experts across the globe and ask them the same set of 7 questions regarding data and data integration in the business. We want to derive insights from their direct experiences and expertise that will help companies, both B2B and B2C at all stages of their evolution. We are also curious to see if their answers are similar or different. These interviews will be featured on this website as a series.
Interview with Guy Pearce, Managing Partner REData Performance Consulting
Guy Pearce is the Managing Partner of REData Performance Consulting, where he finds innovative ways for data and analytics to create measurable business value for his clients, either as a strategic enabler, as a means to generate revenue, as a means to optimize costs or as a means to mitigate risk. His strategic outlook and performance track record has led to him serving on a diversity of Corporate Boards in Banking, Retail and Financial Services, on Board Committees for Audit, Risk, Credit, Leadership and ICT, and also as CEO within a publicly traded retailer, which he brought to profitability at the height of the global recession in 2010. Furthermore, his Audit and Risk background means that he has first hand knowledge of how data governance and risk management can ensure that data and processes are of the requisite quality for strategic business intelligence and decision-making. He was recently voted onto the Board of the International Institute of Business Analysis and serves on its Audit and Risk Committee.
Robin Smith: Has the term Big Data been over hyped? There a sense of “Big Data” fatigue, backlash even, that seems to be becoming more prevalent. Is Big Data relevant?
Guy: Technology is rather (in)famous for its hype cycle, and it is indeed fair to say that Big Data has been over-hyped, especially given market commentary suggesting “Big Data fatigue”. However, I really think big data is something special, firstly because data is going to continue to grow exponentially all around us, whether we like it or not, and secondly because Big Data technology is actually quite amazing, and it can only get better. Being able to makes sense of all this data in a particular context is where the power of the technology really is. However, what many don’t seem to realize is that the technology is not a silver bullet, and probably never will be. Simply installing and configuring it doesn’t mean you’ll suddenly have the world’s data at your fingertips, ready to create extraordinary insights that will enable you to leapfrog your competition. It still takes a human that understands both the business and technology worlds sufficiently well to be able to identify the most relevant data sources and direct the technology to where the business needs it most, whether it be in sales, marketing, operations, risk, research, HR … the list keeps growing.
Robin Smith: Do you really need data scientists as part of your big data strategy? What are the characteristics required of a data scientist? Does this have implications for our educational systems?
Guy: If you’re really at the bleeding edge of the data spectrum, particularly in a research setting, then analysing extraordinary masses of data that cannot easily be categorized in ways we may be used to in a corporate setting, then yes, I would argue that you do need that new breed of analytics capability that a data scientist would bring to the table. However, this is not to say that all applications of big data need a data scientist, as a competent statistician and mathematician can create powerful models that merge external data with internal data, whether structured or unstructured, to create the basis for business insights that the organization might never have had before. In business, the latter matters much more than finding obscure patterns in data that may have no direct bearing on the challenges the business is faced with. Again, these patterns may be very interesting on the bleeding edge, but there’s a big difference between interesting, and having actionable insights that can positively impact the strategy, revenue, cost and risk profiles of the organization.
Robin Smith: Is the relational database, the foundation of the data warehouse in the small data world, still relevant in a big data age?
Guy: Most certainly! Just because NoSQL databases like Hadoop have arrived, creating tremendous value in some settings, doesn’t mean that relational calculus as it is applied in relational databases no longer has value. Indeed, for structured transactional and reporting data where database performance, random access, and the ability to rapidly update data matter, there is still nothing to beat a relational database. Furthermore, the closer we get to real time processing and reporting, the greater the value of a well-configured relational database in making it a reality. On the other hand, if you have a small number of massive files that are apparently little more than bits and bytes – image, video, audio, sensor, streams, and in some cases, text data – then a NoSQL database like Hadoop would be the platform of choice. Ultimately, any data strategy and associated data architecture that claims to be competitive in a big data age, yet that ignores one or the other platform types is an incomplete strategy, and a tragedy for the company concerned, as its data enablement competencies will be compromised in the medium to longer term by such short-sightedness.
Robin Smith: Given the reputation and organizational risks involved in poorly governed data (privacy, breach, quality), should data governance be a corporate governance imperative? Where should ownership of this risk reside, should a company have Chief Data Officers?
Guy: Using the Target data breach of November 2013 as an example, where over $200 million has already been spent in fixes at the point of breach, investors are rightfully as outraged about the destruction of value as banks are about the credit risk of stolen credit card credentials, and as customers are about their violation of their trust in the organization. Already five banks are leading a class action lawsuit against Target, and affected customers are currently pursuing related class action litigation. Whether shareholders join them is yet to be seen, but the point remains that poor ICT governance has some very serious implications for the organization beyond the basic cost of reparations. This most certainly makes ICT governance (including data) a corporate governance imperative, as stakeholders such as creditors, shareholders and customers are the most negatively affected by poor governance, not to mention the overflow of negative sentiment that could affect an entire industry. Active risk management is therefore an organization-wide imperative, and it is deemed better if risk management ultimately reports into the CEO, to avoid the potential conflict of interest involved in (internal) audit reporting to the CFO. As far as a Chief Data Officer is concerned, I think there is a distinct need for this role, as it is concerned with sourcing, managing and leveraging a totally different type of asset compared with the assets that fall under the stewardship of the CIO and the CTO.
Robin Smith: Is big data only for big companies with deep pockets?
Guy: In general, “big data” as a paradigm benefits all organizations that see data-driven activities as a significant means of enhancing their competitiveness. If you have deep pockets, you will most certainly be targeted by the large vendors. However, if needed, the cloud makes an enormous amount of big data storage and processing capacity available at much lower costs. What’s more important though is for the organization to first articulate exactly what it is trying to achieve. This is encapsulated in the process of strategy development, which, if properly conducted, will define the gap between the capabilities it has and the capabilities it needs to achieve its objectives. As part of the capability assessment process, the types of data – internal, external, structured and unstructured – required to achieve the organizations objectives will be determined. In my experience, an awful lot can often be achieved with the client’s existing data infrastructure. Furthermore, it’s not about data volumes, but rather how the organization is able to leverage data as a key enabler of its strategy. Even integrating “small” disparate datasets can drive significantly better insights, and if appropriately put into action, these can result in enhanced competitiveness. Doing this may require no incremental costs, yet you will have put two attributes of big data – data variety and data fusion – to very good use.
Robin Smith: How has Data changed the way business should look at their systems?
Guy: In days past, systems produced data almost as a side effect, but as time progressed, more and more of that data was put to good business use. Whether it was Six Sigma and lean manufacturing, Economic Order Quantities (EOQ) or risk of credit default, data and analytics began to play an increasingly important role in business. Data was becoming much more than simply the raw material for reporting what happened the previous month, and as econometrics methods became mainstream, it could also be used to forecast things like resource requirements and demand. This process of using data for management decision-making ushered in an era of Management Information Systems, Executive Information Systems, and more recently, Business Intelligence systems. At the forward edge of Business Intelligence today is data visualization technology, which attempts to build on the idiom “a picture paints a thousand words” into practice. Evaluating the architectural fit of a transactional system today is done with more interest in whether it can facilitate Business Intelligence, or at the very least, how well it provides for the accessibility and usability of its data, preferably in non-proprietary databases. The latter is important in the context of integrating the transactional data with other data, for enhanced business insights. Furthermore, there is an increasing demand that systems facilitate the process of transaction-level data governance, such as providing information on various dimensions of quality. This is an important matter, as data quality is often determined by the disciplines surrounding the transactional system.
Robin Smith: Data ownership and value has become the latest discussion point in the data hype cycle. Has the accounting and legal paradigm changed enough for data to be defined as an asset on the balance sheet and has ownership been clearly delineated from a legal perspective?
Guy: The question of data ownership has been with us for decades already, possibly because data is, quite simply, power, and with power, comes internal politics. This question has often interfered with the ability of organizations to create the dream of an enterprise-wide data warehouse and a single view of the customer, with all of the benefits such a construct confers to the organization, and its customers. Organizations with strong leadership have indeed achieved enterprise-wide data warehouses, but almost 25 years into mainstream data warehousing, they still seem to be more of the exception. There is therefore a right answer from a data productivity perspective, being that data is the property of the organization, but this varies from case to case. However, there is a different angle to the question though in an age of privacy, and that is that customers are beginning to lay claim to their personal data held by organizations, and in Canada today, customers can legally ask companies to disclose the data the companies hold about them. This and other trends are reinforcing the formal data governance and privacy movement, where organizations increasingly recognise that they are stewards of all sorts of data, and that due care and respect is required in how that data is stored, processed and used. This is because some data has value, with some of that data being disposable, and other data having longer term value, something like an asset. The simple proof of this is that organizations sell data to other organizations. Marketers are close to hitting the mark on determining the value of data when they calculate customer equity, defined as the value that the organization’s customers can create for the organization in the form of sales, over a defined future time period. Marketers then action customer equity by leading targeted campaigns towards those customers, in the expectation of a sale. Now whether the embedded value of data, the financial value of data will ever be accounted for on the balance sheet is anyone’s guess.
About REData Performance Consulting
Based in Toronto, we are experts at monetizing (big) data and in aligning your data initiatives with your strategic imperatives. In this way, we have created more than $150 million directly by means of data leverage, and in the words of a major B2B client of one of our clients, a data strategy we developed significantly elevated the competitiveness of our client by driving better customer retention!
Our expertise extends to data strategy, architecture, sourcing, storage, organization and governance. Given a business problem, we conduct analysis and analytics, derive business insights, and turn them into actionable, quantifiable value in marketing, operations, HR, finance and risk. Then, from an end-user perspective, we deploy it all as Management Information or Business Intelligence.
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