I don’t know about you, but when I finish my time at a conference, I often feel a bit let down. Often, I find myself sorting through what I heard or learned, looking for one or maybe two pearls of wisdom. But last week, I was amazed as I took stock of this year’s Informatica World to find that I actually got 10 great pieces of wisdom that could be useful to anyone trying to be digital disruptor at their firm. One thing before, I get started. To protect the innocent, I will only accredit names to those that would be fine with me sharing what they said publically. So let’s get started.
“Businesses are moving from the age of productivity to the age of engagement”, said Sohaib Abbasi. Sohaib’s comment clearly pick up on a recent theme of Geoffrey Moore, but goes further by asserting that enterprises that will have a continuing “right to win” will be in front of all things data. These winners, Sohaib claims, “realize that traditional approaches to data no longer work. This means that they know that they need be to master all things data—transactions, social, and even machine data”. In a retail setting, this means that they not just know a customer’s transactions but what the customer likes and who their friends are.
- “The data explosion is here. Winners will take advantage of this change. They will drive tangible benefits for their businesses. The world today is about analytics, interactions, and mobility”.
- “We need a new way to think about analytics. Analytics needs to be about fast, smart decisions”,said Tom Davenport. Yet, he said there is a problem with big data? What is next if we continue to call it big data–hellabyte, muchabyte, and lots of byte”?
- “Data is now a business driver and we need to treat data as a business asset”. Given this, being a successful digital disruptor requires a “common business vision of what we want to accomplish and a true partnership between the business and IT”.
- “There is a supply chain for data. This supply chain is about transforming data so it is at the right place at the right time”, said Narendra Mulani, Senior Managing Director, Accenture Analytics.
- “The IoT is a big word—it is a business risk but has the potential to change everything if we have a data strategy to deliver business value”, Steven Levitt.
- “Bill Ford says cars are rolling collections of sensors”, said Tom Davenport. A true Internet of Things?
- “We need to embed analytics into the business operating model. Doing so will align organizational process and technology and turn data into an enterprise driver”, Paul Barrett, Accenture Analytics.
- “GE has determined that data should receive the same care that they build jet engines”, Scott Barber, GE
- “With data science today, we have the opportunity to create a new age of reason…Democratization of data means that we can create change in our underpants”, said Jake Porway, DataKind.
There are a lot of big ideas in these quotes. Let me, therefore, try to summarize. The goal of enterprise software is changing from increasing productivity to increasing engagement between companies and their employees and companies and their customers. Because of mobility, users are demanding that they have their data now whether it is for small data or big data. And regardless of data type, data has to be great—for this reason, there needs to be supply chain of data. The IoT has to have specific goals to succeed. And increasingly data measurement needs to happen at the business operating model level. So with great data and great data science, the important wins will come through the democratization of data access and data use.
I remember being an A’s fan during the Moneyball era. When my wife and I saw the movie, we kept asking each other do you remember going to this game or that game. Prior to the movie, neither of us knew what was taking place in the A’s back office. All we knew was it was hard not to want to be part of this team with such a low payroll, kids drumming at every game, and irrepressible will to win even though the odds were stacked against them.
CEO leadership is needed to push analytics thinking
Just like what happened during Moneyball, I am increasing finding that analytics do not happen in a vacuum. Leadership is needed to push analytical thinking. There needs to be an orientation to analytics and this needs to come from the top for an enterprise analytics approach to take hold and to grow. Clearly, not all organizations need to have their chief analytics officer reporting directly to the CEO, but there needs to be bias to use data rather than gut feel. And this bias needs to be set at the top of the organization. Otherwise, organizations end up with enterprise fiefdoms of information.
Last week, I was sitting with two IT leaders for a major mutual fund company. I was talking to them about the importance of analytics. While both agreed with me, they said that some managers prefer to use their intuition and experience versus data. Imagine that—the most data centric type of enterprise has managers preferring intuition over data. However, CEOs are starting to act as change agents here. Marc Benioff says, “I think for every company, the revolution in data science will fundamentally change how we run our businesses. Our greatest challenge is making sense out of data. We need a new generation of executives to understand and lead through data”.
Brian Cornell is a great case study
One of the leaders of the analytics vanguard is Brian Cornell. “Analytics have been a central part of Cornell’s approach”. When he headed Sam’s Club, he used analytics to improve the unit’s customer-insight system. The results were so good that Wal-Mart moved all of their analytics teams under Cornell. According to Stuart Aitken, Cornell does not just look at the data. He goes beyond the data and asks hard questions of customers and those on his team. Cornell method involves taking the clues he gathers from customer conversations and using analytics to look for broader patterns that would reveal problems and opportunities.
Cornell’s emphasis on analytics was a key reason why Target board’s hired him. His record with analytics is amazing. This included his ability to use data to expand in house brands and reverse sales declines at each company that he worked for. At Sam’s Club, for example, he made it into the fastest growing division of Wal-Mart. Presently, Cornell is using data and analytics to look for areas where Target can reestablish it’s right to win.
Change moment for CEOs
We are clearly at a change moment for CEOs. In the past, CEOs and their managers relied on backward facing reporting to drive forward facing performance. But today, timely data exists to drive forward facing performance—especially, if the analytics are placed on top of them to show connections and predict near term impacts. Whether it be for the front office or the back office, with great data—data which is trustworthy and timely—it is possible for CEOs and their leadership teams to be the captain of the ship. It is possible with great data and a willingness to dig into what the great data represents to see the business icebergs ahead and to take action not only corrective action, but as well, to make use of the opportunities and trends that they represent. Clearly, with great data, analytical CEOs and their teams can develop strategies that can be the basis for new approaches to winning businesses.
So how can you become the analytical leader that your enterprise needs? To point you in the right direction, here are four practices that will fuel your strategic use of data. The linked research combines the latest research from the Economist Intelligence Unit and a global survey of IT professionals and C-level executives. From this research, the connection between the strategic use of data and financial performance will become absolutely clear.
Author Twitter: @MylesSuer
Recently, I got to attend a summit of governmental IT leaders. These public sector leaders just like their peers within the private sector, see data increasingly as a mechanism for delivering more effective and efficient “business capabilities”. In this post, I will share some of the key takeaways from the presenters about the importance of creating what I like to call the “data ready enterprise”.
The Deputy CIO for the FCC said that they are moving from transforming their organization’s technology processes to transform their organization’s business processes. Taking this step can drive out better alignment between IT and the rest of the organization. “The problem for large public and private organizations is that they cannot stay rooted in the past”. For public sector organizations, “we need to provide greater transparency into what we do. We need to create at the same time a freer flow of data with our customers”. At the same time, “we need to drive business outcomes”. This means enabling better, faster, and yes, cheaper at the same time as they adapt, buy, and create. And we need to show that on the continuum of change that we are truly “leaning forward”.
The emergence of the governmental CDO
The Deputy CIO, USDA said that 11 Federal Agencies now have a CDO function. She said that overall 25% of enterprises have a CDO. Like others in the government, USDA is participating in the “Open Data Initiative”. To make this reality, she said that they have creating data stewards within USDA. She said that they are trying, as well, to limit the number of copies for the same dataset—a common problem across all enterprises.
Next up was one of these Federal CDOs. The CDO for the Department of Energy started his presentation by saying IT is all about the data dummy. “People need to remember that this is why the tech is here anyway”. He shared his prior experience at Capital One, a predicative analytics leader. He said that today 80% of the time for a data scientist is just finding the data. He recommended that organizations need to manage first data and then the technology. This is the opposite of the way things are done at many organizations. He went on to say that we need to stop managing the data in silos. He effectively asserted the need for an “enterprise analytics capability” to do this. He said face it, “data is a business asset and today, data management is, therefore, part of business accountability”
Data is the fuel for decision making
The Deputy Director, OUSD, DoD, said that he needs to generate real value for his stakeholders. For this reason, those in the government need to see “data as the fuel that drives the decision making”. In today’s enterprises, data is the currency. For many, they need to be prepared to work with what we have. My department leaders believe that we are here to effectively guide them through decisions. For this reason, he starts by discussing the data first in these discussions.
Organizations need to care about data quality
Fannie Mae’s Data Quality Service Manager shared how his organization has come to really care about data quality. He said that they have needed this to be an enterprise wide thing. At their organization, data affects the quality of work and the quality of life. From their experience, data quality like enterprise architecture starts with business architecture. Organizations can either be proactive or reactive for data quality. Good data management uses event management to determine when the rules do not work. And good data quality increasingly needs to become self-service—it should be run by the business so they trust the data that is output.
USPS needed a world class data system to be have sustainable right to win
The USPS, Senior Technology Architect, said that the USPS needed to change in order to survive. Strategically, USPS needed to move from a letter centric business to a package centric business. To be a world class package delivery business, they needed a world class package tracking system. This meant aggressively moving to bar codes and creating a world class supply chain that captures package scans at every step along their journey. Their legacy system architecture was too slow to deliver this and had long lag times for data—furthermore, it proved costly because it demanded regular downtime. Their new architecture, developed to respond to the above deficiencies, includes what they call a scan event architecture. This is capable enough to know when things have gone south. They now have 99.5% availability while managing 150,000,000 tracking events per day. With this performance, they are starting to earn business away from Internet Based Businesses. Their next step is to use package scan data to predict not only the day but the time window for delivery.
Public sector IT organizations just like for profit businesses need their data to be decision ready. And being decision ready means having data that is trustworthy and timely regardless of the enterprise nature. Additional Materials
Solution Page: Corporate Governance
Solution Page: Data Scientist Data Discovery
Blogs and Articles IT Leadership Group Discusses Governance and Analytics
The need for transformation has reached the tipping point
For more and more companies, the need for financial transformation has already reached a tipping point. Innovation Enterprise says a major driver for this is “the pressure on finance departments to become more strategic”. This has caused financial departments to embark “on transformational processes to bring the (finance) function in line with the needs of today’s business environment. Much of this has to do with the streamlining of IT processes and automating repetitive tasks that aid execution”.
At the same time, CFOs tell us that they want to improve the quality of financial and performance insights obtained from the data they produce. Gartner confirmed this by asking CFOs to identify where they see the need for technology improvement. Top areas for CFOs were Business Intelligence (BI), Analytics, and Performance Management. Gartner claims the organizations that it has talked to “are still struggling to make progress with BI and analytics”. And while many IT organizations have made initial investments here, they have tended to be, according to Gartner, tactically focused and therefore, have not addressing more fundamental issues including data quality and data consistency.
Gartner claims these issues have required CFOs and finance teams to work closely with BI specialists within the IT organization. Often quarterly reporting or profitability analysis requires manual pulls of data. Most finance departments are saddled with legacy environments. And the supporting processes for these systems remain difficult, manual, and time intensive. When I worked at very large computer manufacturer, we had to give the finance team at least 2 months to input product changes into the financial system. Clearly, once you move from the general ledger or general ledgers in more complex organizations, you find that much of what takes place within finance is still a manual process. According to a recent study, an amazing “51% of CFO survey respondents say that their collecting, storing, and retrieving financial and performance data at their company is primarily a manual and/or spreadsheet-based exercise” (The Intelligent Finance Organization, KPMG, page 16).
For those that are not in finance, this must be a shocker. But it should come as no shock that many financial organizations are undertaking timely and expensive financial transformations at this time. The goal for these transformations should not necessarily to replace their ERP and other financial systems but instead to improve their overall financial effectiveness and efficiency.
On the effectiveness side, CFOs tell us that they want to improve the quality and timeliness of data that they create so they can take on a more strategic role in the enterprise. I have already shared about how CFOs have a data trust issue.
On the efficiency side, CFOs want to create more efficient financial business processes. So what is holding them back in being more efficient? In most cases, it is the complexity of managing existing legacy financial and business systems and ensuring that everything is in synch between them. What financial organizations have told us is that they do not want to replace their legacy systems but they want instead to replace how they manage these systems so they can get the “head room” needed to be more efficient. What they want specifically is the ability to eliminate the complexity of these systems daily care and feeding.
A financial transformation project, therefore, should aim at improving the supporting financial processes and automate as much as possible around the financial processes that surround the general ledger. When asked what has driven these financial processes to become so cumbersome to manage, we have been told business change—reorganization, new businesses, acquisitions, new products, and new business process. It is managing the change to the financial structures, financial metadata, and hierarchies.
In talking with financial departments, we have heard that many components of the process are completely manual. Every change, for example, has to be managed across enterprises geographies, FP&A systems, accounting systems, and more than one business systems. It is a huge job making everything synch if done manually. One company that we talked said they needed for all of this not to be manual. Another said, automating from a single version of truth would be extremely valuable. Right now, we connect to multiple systems and it is difficult to maintain consistency between each source of truth. A number of things do not always tie together—“I admit it we have system inconsistencies”.
Financial departments are asking for a master of everything that touches financial systems including their SaaS systems. This capability would make the management of changes entire automated and over time system dampen out inconsistencies completely. For most medium to large organizations, this is a big deal. At the same time, they need the ability to test, schedule, and then recast financial results easily due to planned business change. So much of this today is managed in spreadsheets. With automation, financial departments want as well the ability to eliminate manual notifications and review processes for changes to entities and the general ledger. As well they want traceability and tractability for all changes made. One finance department said that they need an audit trail for their management of changes made. They said just think about the effort to maintain hierarchies that go into the expense report system alone. It easily touches as many as 20 systems. Finally, they said it would nice to have transparency to state before a change. This organization said that it is going to become more important to maintain lineage analysis for audit trail.
Do you want to hear more
Deloitte and eBay will jointly present on the solution that they have put together for financial transformation on Tuesday the 12th at 2:40 p.m. in Las Vegas at Informatica World. Their presentation will include their vision for Financial Transformation. As well it will include how to put together a data led approach, their design decisions, and their leading practices. Finally, they will discuss how they built their solution to manage SAP data on top of the Informatica MDM solution.
Big data is receiving a lot of press these days including from this author. While there continues to be constructive dialog regarding whether volume, velocity, or variety are the most important attributes of big data movement, one thing is clear. Constructed correctly, big data has the potential to transform businesses by increasing sales and operational efficiencies. More importantly, when big data is combined with predictive analytics, big data can improve customer experience, enable better targeting of potential customers, and improve the core business capabilities that are foundational to a business’s right to win.
The problem many in the vanguard have discovered is their big data projects are fraught with risk if they are not built upon a solid data management foundation. During the Big Data Summit, you will learn directly for the vanguard of big data. How have they successfully transition from the traditional world of data management to a new world of big data analytics. Hear from market leading enterprises like Johnson and Johnson, Transamerica, Devon Energy, KPN, and Western Union. As well, hear from Tom Davenport, Distinguished Professor in Management and Information Technology at Babson College and the bestselling author of “Competing on Analytics” and “Big Data at Work”. Tom will share in particular his perspective from interviewing hundreds of companies about the successes and failures of their big data initiatives. Tom Davenport initially thought big data was just another example of technology hype. But his research on big data changed his mind. And finally hear from big data thought leaders including Cloudera, Hortonworks, Cognizant, and Capgemini. They are all here to share their stories on how to avoid common pitfalls and accelerate your analytical returns in a big data world.
To attend in person, please join us on Tuesday the 12th at 1:30 in Las Vegas at the Big Data Summit. If you cannot join us in person, I will be share live tweets and videos through twitter starting at 1:30 PST. Look for me at @MylesSuer on twitter to follow along.
What is Big Data and why should your business care?
Big Data: Does the emperor have their clothes on?
Should We Still be calling it Big Data?
CIO explains the importance of Big Data to healthcare
Big Data implementations need a systems view and to put in place trustworthy data.
The state of predictive analytics
Analytics should be built upon Business Strategy
Analytics requires continuous improvement too?
When should you acquire a data scientist or two?
Who Owns Enterprise Analytics and Data?
Competing on Analytics: A Follow Up to Thomas H. Davenport’s Post in HBR
Thomas Davenport Book “Competing On Analytics”
Analytics Stories: A Banking Case Study
Analytics Stories: A Financial Services Case Study
Analytics Stories: A Healthcare Case Study
Author Twitter: @MylesSuer
How can analytics transform the world of accounting?
As I have shared elsewhere within this series, businesses are increasingly using analytics to improve their internal and external facing business processes and to strengthen their “right to win” within the markets that they operate. To do this, many firms start with their enabling business capabilities. Clearly, “analytics can help transform just about any part of a business or organization. Many organizations start where they make money—in customer relationships” (Analytics at Work, Harvard Business Review Press, page 9).
The world of accounting is no different. However, in Grant Thornton’s case, it has determined that better customer data can actually help it improve its business capabilities system related to both back office and front office processes. For purposes of this discussion, “a capability is the ability to reliably and consistently deliver a distinctive outcome relevant to the business” (The Essential Advantage, Harvard Business Review Press, Page 14).
Relating Client profitability to quality of work performed
In terms of improving its back office processes, Grant Thornton wants to get a better understanding of client profitability as well as the quality of work that is actually being performed for each of its clients. These two measures are of course related over the longer haul. As Theodore Levitt indicated in Marketing Myopia, “the purpose of a business is to create and keep a customer”. And client profitability and life time value of a customer on a longer term basis is related to quality of work performed. To improve both, Grant Thornton is creating and providing access to a number of business critical metrics around staffing, quality of delivery, and profit versus cost of each customer engagement.
Accounting for the total customer relationship
At the same time, Grant Thornton has determined that they need to use their customer data in order to get to know their customers better. At Informatica, we like to call this the total customer relationship. Just like other service based businesses, Grant Thornton wants to improve its ability to cross sell and upsell. For example, if I am doing audit work, can I also do tax or other business services. To make this reality, they need like just about every other business a single view of customer.
Accounting from the numbers not gut feel
To make both of these a reality, Grant Thornton has started by getting its critical information out of its applications and into an operational data store—a database designed to integrate data from multiple sources. They are using a mix of traditional ETL tools and new cloud delivered solutions to pull data out of cloud based systems like Microsoft Dynamics. This is giving them truly a hybrid data collection environment. With all of their data in hand, they then have taken their data and stuck it into a data warehouse for reporting. From here they have an initiative to build analytical dashboards for business leaders and customers alike. Their goal is to move decision making from gut feel to data. According to Tom Davenport, “our research suggests that 40 percent of major decisions are based not on facts but on a managers gut”. (Analytics at Work, Harvard Business Review Press, page 1).
As I have discussed, firms are starting to use analytics to better manage their core business capabilities. For this reason, analytics are more and more foundational to a business’s right to win. Clearly, the ability to use analytics to keep existing customers by measuring and improving service quality and increase the degree of cross sell is core to Grant Thornton’s ability to retain and grow its existing business.
Download: Grant Thornton Case Study
Author Twitter: @MylesSuer
Improving Order to Cash matters regardless of market cycle
Order to Cash (OTC) matters to today’s CFOs and their financial teams even as CFOs move themselves from an expense and cost reduction footing to a manage growth footing. As the business process concerned with receiving and processing customer sales, having a well-functioning OTC process is not only about preserving cash, but it is also about improving the working capital delivered to the bottom line. This is something which is necessitated by successful growth strategies. Specifically, OTC helps to provide the cash flow needed to quicken collections and improve working capital turnover.
This drives concrete improvements to finance metrics including Days Sales Outstanding (a measure of the average number of days that a company takes to collect revenue after a sale has been made) and the overall cost of collections. It should be clear that a poorly running order to cash process can create tangible business issues. These include but are not limited to the following:
- Accuracy of purchase orders
- Accuracy of invoices
- Volume of customer disputes
- Incorrect application of payment terms
- Approval of inappropriate orders
- Errors in order fulfillment
CFOs tell us that it is critical that they make sure that “good cash management is occurring in compliance with regulation”. It is important as well to recognize that OTC cuts across many of the primary activities of the business value chain—especially those that related to sales, marketing, and services.
How do you improve your order to cash process?
So how do financial leader go about improving OTC? They can clearly start by looking at the entire OTC process from quote order, process order, fulfill order, invoice customer, receive and apply cash, and manage credit and collections. The below diagram shows the specific touch points where the process can be improved—each of these should be looked at for process or technology improvement.
However, the starting point is where the most concrete action can be taken. Fixing customer data fixes the data that is used by each succeeding process improvement area. This is where a single, connected view of customer can be established. This improves the OTC process by doing the following:
- Fixes your customer data
- Establishes the right relationships between customers
- Establishes tax and statutory registrations and credit limits
- Prevents penalties for delivering tax documents to the wrong placeCustomer Data Mastering (CDM) does this in the following way. It provides a single view of customers, 360 degree view of relationships as well as a complete view of integrative customer relationships including interactions and transactions.
CDM matters to the CFO and the Business as a whole
It turns out that CDM does not just matter to OTC and the finance organization. It matters as well to the corporate value chain by impacting the following primary activities including outbound logics, marketing and sales, and service. Specifically, CDM accomplishes the following:
- It reduces costs by reducing invoicing and billing inaccuracies, customer disputes, mailing unconsolidated statements, sending duplicate mail, and dealing with returned mail
- Increases revenue by boosting marketing effectiveness at segmenting customer for more personalized offers
- Increases revenue by boosting sales effectiveness by making more relevant cross-sell and up-sell offers
- Reduces costs by boosting call center effectiveness by resolving customer issues more quickly
- Improves customer satisfaction, customer loyalty and retention because employees are empowered with complete customer information to deliver great customer experiences across channels and touch points
So as we have discussed, today’s CFOs are finding that they need to become more and more growth oriented. This transition is made more effective with a well function OTC process. While OTC impacts other elements of the business too, the starting point for fixing OTC is customer data mastering because it fixes elements of the data portion of this process.
Data Science should change how your businesses are run
The importance of data science is becoming more and more clear. Marc Benioff says, “I think for every company, the revolution in data science will fundamentally change how we run our business”. “There’s just a huge amount more data than ever before, our greatest challenge is making sense of that data”. He goes on to say that “we need a new generation of executives who understand how to manage and lead through data. And we also need a new generation of employees who are able to help us organize and structure our business around data”. Mark then says “when I look at the next set of technologies that we have to build at Salesforce, it is all data science based technology.” Ram Charan in his article in Fortune Magazine “says to thrive, companies—and the execs who run them, must transform into math machines” (The Algorithmic CEO, Fortune Magazine, March 2015, page 45).
With such powerful endorsements for data science, the question you may be asking is when should you hire a data scientist or two. The answer has multiple answers. I liken data science to any business research. You need to do your upfront homework for the data scientists you hire to be effective.
Create a situation analysis before you start
You need to start by defining your problem—are you losing sales, finding it takes too long to manufacturer something, less profitable than you would like to be, and the list goes on. Next, you should create a situational analysis. You want to arm your data scientists with as much information as possible to define what you want them to solve or change. Make sure that you are as concrete as possible here. Data scientists struggle when the business people that they work with are vague. As well, it is important that you indicate what kinds of business changes will be considered if the model and data deliver this results or that result.
Next you need to catalog the data that you already have which is relevant to the business problem. Without relevant data there is little that the data scientist can do to help you. With relevant data sources in hand, you need to define the range of actions that you can possibly take once a model has been created.
Be realistic about what is required
With these things in hand, it may be time to hire some data scientists. As you start your process, you need to be realistic about the difficulty of getting a top flight data scientist. Many of my customers have complained about the difficulty competing with Google and other tech startups. As important, “there is a huge variance in the quality and ability of data scientists”. (Data Science for Business, Foster Provost, O’Reilly, page 321). Once you have hired someone, you need to keep in mind that effective data science requires business and data science collaboration. As well, please know that data scientist struggle when business people don’t appreciate the effort needed to get an appropriate training data set or model evaluation procedures.
Make sure internal or external data scientists give you an effective proposal
Once Once your data scientists are in place, you should realize that a data scientist worth their salt will create a proposal back to you. As we have said, it is important that you know what kinds of things will happen if the model and data delivery this results or that result. Data scientist in turn will be able to narrow things down to a dollar impact.
Their proposal should start by sharing their understanding of the business and the data which is available. What business problems are they trying to solve? Next the data scientist may define things like whether supervised or unsupervised learning will be used. Next they should openly discuss what efforts will be involved in data preparation. They should tell you here about the values for the target variable (whose values will be predicted). They should describe next their modeling approach and whether more than one model is be evaluated and then how models will be compared and final model be selected. And finally, they should discuss how the model will be evaluated and deployed. Are there evaluation and setup metrics? Data scientists can dedicate time and resources in their proposal to determining what things are real versus expected drivers.
To make all this work, it can be a good idea for data scientist to talk in their proposal about likelihood because business people that have not been through a quantitative MBA do not understand or remember statistics. It is important as well that data scientist before they begin ask business people the so what questions if the situation analysis is inadequate.
Leading an internal analytics team
In some cases, analytical teams will be built internally. Where this occurs, it is really importantly that the analytic leader have good people skills. They need as well to be able to set expectations that people will be making decisions from data and analysis. This includes having the ability to push back when someone comes to them will a recommendation based on gut feel.
The leader needs to hire smart analysts. To keep them, they need a stimulating and supportive work environment. Tom Davenport says analysts are motivated by interesting and challenging work that allows them to utilize their highly specialize skills. Like millenials, money is nice for analysts but they are more motivated more by exciting work and having the opportunity to grow and stretch their skills. Please know that data scientists want to spend time refining analytical models rather than doing simple analyses and report generation. Most importantly they want to do important work that makes a meaningful contribution. To do this, they want to feel supported and valued but have autonomy at work. This includes the freedom to organize their work. At the same time, analysts like to work together. And they like to be surrounded by other smart and capable collogues. Make sure to treat your data scientists as a strategic resource. This means you need development plans, career plans, and performance management processes.
As we have discussed, make sure to do your homework before contracting or hiring for data scientists. Once you have done your homework, if you are an analytic leader, make sure that you create a stimulating environment. Additionally, prove the value of analytics by signing up for results that demonstrate data modeling efficacy. To do this, look here for business problems that will lead to a big difference. And finally if you need an analytics leader to emulate, look no further than Brian Cornell, the new CEO of Target.
Myles in Twitter: @MylesSuer
Data and Information becoming a key corporate asset
According to Barbara Wixom at MIT CISR, “In a digital economy, data and the information it produces is one of a company’s most important assets”. (“Recognizing data as an enterprise asset”, Barbara Wixom, MIT CISR, 3 March 2015). Barbara goes onto suggest that businesses increasingly “need to take an enterprise view of data. They should understand and govern data as a corporate asset, even when data management remains distributed”.
CIOs are not the enterprise data steward
Given that data is a corporate asset, you might expect this would be an area for the CIO’s leadership. However, I heard differently when I recently met with two different groups of CIOs. Regardless of whether the CIOs were public sector or private sector, they told me that they did not want to be the owner of enterprise data. One CIO succinctly put it this way, “we are not data stewards. Governance has to be done by the business—IT is merely the custodians of their data”. These CIOs claim that the reason that the business must own business data and must determine how that data should be managed is because only the business understands the business context around the data.
Given this, the CIOs that I talked to said that IT should not manage data but “should make sure that what the business needs done gets done with data”. CIOs, therefore, own the processes and technology for ensuring data is secured and available when and where the business needs it. Debbie Lew from ISACA put it this way, “IT does not own the data. IT facilitates data”.
So if the management of data is distributed what is the role of the CIO in being a good data custodian?
COBIT 5 provides some concrete suggestions that are worth taking a look at. According to COBIT, IT should make sure information and data owners are established and that they are able to make decisions about data definition, data classification, data security and control, and data integrity. Additionally, IT needs to ensure that the information system provides the “knowledge required to support all staff in their work activities.”
IT must create facilities so knowledge can be used
This means IT organizations need to create facilities so that knowledge can be used, shared and updated. Part of doing this task well involves ensuring the reliable availability of useful information. This should involve keeping the ratio of erroneous or unavailable information to a minimum. Measuring performance here requires looking at the percent of reports that are not delivered on time and the percent of reports containing inaccuracies. These obviously need to be kept to a minimum. Clearly, this function is enabled by backup systems, applications, data and documentation. These should be worked according to a defined schedule that meets business requirements.
To establish a level of data accuracy, that is acceptable to business users, starts by building and maintaining an enterprise data dictionary that includes details about the data definition, data ownership, appropriate data security, and data retention and destruction requirements. This involves identifying the data outputs from the source and mapping data storage, location, retrieval and recoverability. It needs to ensure from a design perspective, appropriate redundancy, recovery and backup are built into the enterprise data architecture.
IT must enable compliance and security
COBIT 5 stresses the importance of data and information compliance and security. Information needs to be “properly secured, stored, transmitted or destroyed.” This starts with effective security and controls over information systems. To do this, procedures need to be defined and implemented to ensure the integrity and consistency of information stored in databases, data warehouses and data archives. All users need to be uniquely identifiable and have access rights in accordance with their business role. And for business compliance, all business transactions need to be retained for governance and compliance reasons. According to COBIT 5, IT organizations are chartered to ensuring the following four elements are established:
- Clear information ownership
- Timely, correct information
- Clear enterprise architecture and efficiency
- Compliance and security
There needs to be a common set of information requirements
But how are these objectives achieved? Effective information governance requires that the business and IT have a strong working relationship. It, also, requires that information requirements are established. Getting timely and correct information often starts by improving how data is managed. Instead of manually moving data or creating layer over layer of spaghetti code integration, enterprises need to standardize a data architecture that creates a single integration layer among all data sources.
This integration layer increasingly needs to support new sources of data too and be able to do so at the speed of business. Business users want trustworthy data. An expert on data integration “maintains that at least 20 percent of all raw data is incorrect. Inaccurate data leads data users to question the information their systems provide.” The data system needs to automatically and proactively fix data issues like addresses, missing data and data format problems. And once this has been accomplished, it needs to go after redundancies in customers and transactions. With multiple IT-managed transaction systems, it is easy to misstate both customers and customer transactions. It is also possible to miss potential business opportunities. All of these are required to get accurate data.
Data needs to be systematically protection
Additionally, data need to be systematically protected. This means that user access to data needs to be managed systematically across all IT-managed systems. Typical data integrations move data between applications without protecting the source data systems’ rules. A data security issue at any point in the IT system can expose all data. At the same time, enterprises need to control exactly what data are moved in test environments and product environments. Enterprises must also ensure that a common set of security governance rules are established and maintained across the entire enterprise, including data being exchanged with partners, employees and contractors using data outside of the enterprise firewall.
Clearly, COBIT 5 suggests that CIOs cannot completely divorce themselves from data governance. Yes, CIOs are data custodians but there are clear and specific tasks that the CIO and their staff must uniquely take on. Otherwise, a good foundation for data governance cannot be established.
I recently got to talk to several senior IT leaders about their views on information governance and analytics. Participating were a telecom company, a government transportation entity, a consulting company, and a major retailer. Each shared openly in what was a free flow of ideas.
The CEO and Corporate Culture is critical to driving a fact based culture
I started this discussion by sharing the COBIT Information Life Cycle. Everyone agreed that the starting point for information governance needs to be business strategy and business processes. However, this caused an extremely interesting discussion about enterprise analytics readiness. Most said that they are in the midst of leading the proverbial horse to water—in this case the horse is the business. The CIO in the group said that he personally is all about the data and making factual decisions. But his business is not really there yet. I asked everyone at this point about the importance of culture and the CEO. Everyone agreed that the CEO is incredibly important in driving a fact based culture. Apparent, people like the new CEO of Target are in the vanguard and not the mainstream yet.
KPIs need to be business drivers
The above CIO said that too many of his managers are operationally, day-to-day focused and don’t understand the value of analytics or of predictive analytics. This CIO said that he needs to teach the business to think analytically and to understand how analytics can help drive the business as well as how to use Key Performance Indicators (KPIs). The enterprise architect in the group shared at this point that he had previously worked for a major healthcare organization. When organization was asked to determine a list of KPIs, they came back 168 KPIs. Obviously, this could not work so he explained to the business that an effective KPI must be a “driver of performance”. He stressed to the healthcare organization’s leadership the importance of having less KPIs and of having those that get produced being around business capabilities and performance drivers.
IT needs increasingly to understand their customers business models
I shared at this point that I visited a major Italian bank a few years ago. The key leadership had high definition displays that would roll by an analytic every five minutes. Everyone laughed at the absurdity of having so many KPIs. But with this said, everyone felt that they needed to get business buy in because only the business can derive the value from acting upon the data. According to this group of IT leaders, this causing them more and more to understand their customer’s business models.
Others said that they were trying to create an omni-channel view of customers. The retailer wanted to get more predictive. While Theodore Levitt said the job of marketing is to create and keep a customer. This retailer is focused on keeping and bringing back more often the customer. They want to give customers offers that use customer data that to increase sales. Much like what I described recently was happening at 58.com, eBay, and Facebook.
Most say they have limited governance maturity
We talked about where people are in their governance maturity. Even though, I wanted to gloss over this topic, the group wanted to spend time here and compare notes between each other. Most said that they were at stage 2 or 3 in in a five stage governance maturity process. One CIO said, gee does anyone ever at level 5. Like analytics, governance was being pushed forward by IT rather than the business. Nevertheless, everyone said that they are working to get data stewards defined for each business function. At this point, I asked about the elements that COBIT 5 suggests go into good governance. I shared that it should include the following four elements: 1) clear information ownership; 2) timely, correct information; 3) clear enterprise architecture and efficiency; and 4) compliance and security. Everyone felt the definition was fine but wanted specifics with each element. I referred them and you to my recent article in COBIT Focus.
CIO says they are the custodians of data only
At this point, one of the CIOs said something incredibly insightful. We are not data stewards. This has to be done by the business—IT is the custodians of the data. More specifically, we should not manage data but we should make sure what the business needs done gets done with data. Everyone agreed with this point and even reused the term, data custodians several times during the next few minutes. Debbie Lew of COBIT said just last week the same thing. According to her, “IT does not own the data. They facilitate the data”. From here, the discussion moved to security and data privacy. The retailer in the group was extremely concerned about privacy and felt that they needed masking and other data level technologies to ensure a breach minimally impacts their customers. At this point, another IT leader in the group said that it is the job of IT leadership to make sure the business does the right things in security and compliance. I shared here that one my CIO friends had said that “the CIOs at the retailers with breaches weren’t stupid—it is just hard to sell the business impact”. The CIO in the group said, we need to do risk assessments—also a big thing for COBIT 5–that get the business to say we have to invest to protect. “It is IT’s job to adequately explain the business risk”.
Is mobility a driver of better governance and analytics?
Several shared towards the end of the evening that mobility is an increasing impetus for better information governance and analytics. Mobility is driving business users and business customers to demand better information and thereby, better governance of information. Many said that a starting point for providing better information is data mastering. These attendees felt as well that data governance involves helping the business determine its relevant business capabilities and business processes. It seems that these should come naturally, but once again, IT for these organizations seems to be pushing the business across the finish line.
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