RESEARCH PAPER. Why better data governance means better business

Transcription

1 RESEARCH PAPER Why better data governance means better business Organisations don t leave their cash to look after itself, so why do they do so with data? November 2012 Sponsored by

2 Contents Executive summary Introduction Data growth The price of poor data Governance strategy Data stewards Stakeholder group Governance programme Health check Conclusion About the sponsor, SAP p3 p3 p3 p5 p8 p10 p11 p12 p12 p14 p15 This document is property of Incisive Media. Reproduction and distribution of this publication in any form without prior written permission is forbidden. 2 Computing research paper sponsored by SAP

3 Executive summary The value of data to the organisations that create it has come into renewed focus recently with the hype around big data. Companies are admonished to mine their information resources for new insights and business ideas, but are insights gleaned from a billion bad records actually valuable? The value of these insights will clearly be dependent on the quality of the data they are based on. Yet in a recent survey of Computing s readers among large organisations, around one in four confesses to doing little or no active data quality control. Introduction Of course, not all data is created equal. Some data is of limited value; whereas some is so vital to an organisation s competitive position and operational strength that a rigorous approach to governing and managing this critical asset is essential. In this paper we will refer to that most critical data as enterprise data. Given today s reality of limited time and resources, organisations that want to lead their sectors will actively decide what data is most important and thus worthy of a focused, rigorous and disciplined approach to ensure it is trusted and transparent. This disciplined approach is called data governance. Data governance has the same business objective as corporate governance: to establish and monitor specific responsibilities and accountabilities for the quality and integrity of digital assets, consistent with the same level of rigour afforded to other business assets, namely: people, cash and physical assets, such as buildings, plant and machinery. So the quality and integrity of data assets has a profound impact on the financial health and business integrity of an enterprise. But how many organisations today treat data with the same degree of rigour and governance as, say, cash on the balance sheet? What people, procedures and tools do they have in place to manage data in this way? A Computing survey of 130 IT decision makers shows that large UK organisations often struggle with data governance. They may have realised the value of data, but the processes to manage it accordingly are still a work in progress. Data growth Ninety percent of the world s data was created in the last two years. Indeed the amount of data that organisations have to store is increasing exponentially. This causes firms to review the storage systems that they have in place, and decide how they will cope with the future demand. At the same time, organisations are seeking to plunder this data for potential competitive advantages. To do so, the information must be gathered into some sort of formal data store from which it can be interrogated. Ninety percent of the world s data was created in the last two years Computing research paper sponsored by SAP 3

4 Slightly less than half (43%) of respondents to Computing s survey had one or more siloed data marts or single-domain datawarehouses (e.g. for customer data). Only a fifth (20%) had mature, multi-domain enterprise warehouses, and a similar proportion (19%) had no data warehouses or data marts in place (Fig. 1). Fig. 1 : Which of the following statements best describes data integration in your organisation? 3% 19% 15% 43% We have one or more siloed data marts or single-domain data warehouses (ie covering one area such as customer data) We have a mature multi-domain enterprise data warehouse We have no data warehouses or data marts in place Don t know Other 20% Customer data is the domain most likely (86%) to be covered by a data warehouse or data mart, followed by financial (73%), product (58%) and employee (48%). Customer data accounts for the fastest-growing domains, due to the increasing use of sophisticated transactional websites and social media. It also attracts a high level of interest from organisations due to its strategic importance, leading firms to mine the data for business advantage. Therefore proper management and storage is becoming increasingly important. How much value do businesses place on the data they collect? Asked simply does your organisation treat the enterprise data it collects as a valuable asset? about half of the survey respondents (54%) are confident it does (Fig. 2). 4 Computing research paper sponsored by SAP

5 Fig. 2 : In your opinion, does your organisation treat the enterprise data it collects as a valuable asset? 7% 3% 36% 54% Yes We re learning No I don t know However, around a third (36%) are less confident, answering we re learning and seven percent are brutally honest in answering no. One anecdotal response sums up those who are less sure that their organisation has the right attitude to enterprise data governance: [data] is viewed as a valuable asset but not treated as well as it should be. As this paper illustrates, that is not uncommon among UK organisations and has consequences for their financial health. The price of poor data There is no disputing that corporate data volumes have ballooned as storage costs have shrunk. However, data quantity does not equal quality. Storing and managing such large quantities of data is becoming increasingly burdensome, with too much data divorced from its contextual relationships. Moreover, the quality of data is often compromised, which can impede decision-making and productivity as surely as insufficient data. Customer, product, employee and supplier data are the most likely to suffer from quality problems Computing research paper sponsored by SAP 5

6 Poor quality or inconsistent data can affect any or all of the data categories (domains) in an organisation (Fig. 3). Customer data is the most likely to be affected, according to 52 percent of respondents, with product, employee and supplier data also significant. Fig. 3 : Which of the following key categories of data (data domains) tend to suffer from poor data quality and/or consistency? Customer data Product data Employee data None of these Supplier data Materials data Financial data Vendor data 52% 26% 25% 23% 20% 16% 14% 14% *Respondents could select more than one answer. Materials (stock) and financial data are less likely to suffer from poor quality. Less than a quarter of respondents (23%) said they had no problems with these main data domains. The minor pain points of poorly governed data are unlikely to attract the attention of senior management, but they can lead to significant issues for the business. Invoices may contain the wrong prices; a valuable customer may be accidentally slighted; an opportunity missed; an innovative insight in one niche not learned by the wider business. Unsecure or unreliable data may even compromise the company s brand. The cumulative effect of these errors diminishes the effectiveness, reputation and profitability of the organisation. According to Gartner, documented studies have shown more than $600 billion in lost revenue attributed to data quality among North American companies. 6 Computing research paper sponsored by SAP

7 Indeed, generic loss of productivity is the known adverse affect of poor-quality data most identified by respondents to Computing s survey (49%), followed by slow business decisionmaking (37%) as shown in Fig 4. Fig. 4 : What adverse effects has lack of data or poor quality data had on your organisation? Loss of productivity Slow business decision-making Compliance risks transparency issues Failed application/ data integration Poor business decisions trust issues None Slow to market with new product/service Failed project Lost out to smarter competitor Failed merger or acquisition 49% 37% 33% 24% 22% 22% 13% 11% 5% 0% *Respondents could select more than one answer. However, poor data can also create much more serious problems which threaten the health and reputation of the business and the careers of those involved. Real-world examples of these problems include: Failure to comply with regulations, bringing legal action from customers or industry regulators; Financial results having to be restated with disastrous impact on share price; Decisions made on inaccurate data sending the organisation off in an erroneous direction; A new product or service may be delayed in coming to market; Projects failing; A smarter competitor stealing a valuable customer. Computing research paper sponsored by SAP 7

8 These are not just theoretical issues. A third of respondents (33%) said poor-quality data had led to compliance risks and transparency issues; 22 percent said it had resulted in bad business decisions; 13 percent said it had meant a new product or service had come to market slower than it otherwise would have done; 11 percent said it contributed to the failure of a project; and 5 percent said their organisation had lost out to a smarter competitor. The IT department is not immune from the penalties of poor-quality data. Major IT integration projects will also suffer from poorly governed data with users rejecting systems based on data that does not reflect reality. Twenty-four percent of respondents said an integration or application project had failed due to poor quality data. Less than a quarter of respondents (22%) say their organisation has not suffered adverse affects from poor-quality data. Governance strategy Data governance is not just about avoiding these catastrophes, but also about improving the trust, transparency and competitiveness of the business, and unlocking profound insights from enterprise data. It can even lead to direct savings by eliminating redundant databases and establishing authoritative data sources to improve and accelerate decision-making from analytics and reporting systems. Data governance is a management process that includes people, processes, policies, and metrics for the oversight of enterprise data to improve the business value. The scope of governance spans the life cycle of information assets from creation and capture through to archiving and deletion, as well as quality, security, accessibility and disclosure. Identifying the most critical enterprise data is key to a successful strategy. In a large organisation with disparate data, tackling the whole thing could lead to disaster. That s why the first step in any successful data governance programme is to separate the news (what s important) from the noise (the clutter) and to focus on the most important data called enterprise data. Data governance is about improving the trust, transparency and competitiveness of the business, and unlocking profound insights from enterprise data Here the data governance strategy is based on a direct business need, such as sharing key data among business units to break down silos and improve time-to-market. The data governance strategy could then be rolled out in phases to additional business units to support key cross-functional business processes. The process of data governance will undoubtedly reveal bad data which will need eliminating, good data which should be made fit for enterprise purpose through more rigour, and disparate data which will need integrating. 8 Computing research paper sponsored by SAP

9 Organisations must become aware of where their enterprise data comes from, how it is created, who is responsible for its accuracy and lifecycle, and other vital metadata components that make it easier to find and use relevant data. According to the survey, responsibility for ensuring data accuracy and quality varies across the sample of organisations (Fig. 5). Fig. 5 : In your organisation, who is responsible for monitoring the quality/integrity of data assets? The business unit that uses the data IT department The people who create the data Data governance officer/ team Compliance officer/team A governance committee the constitution of which depends on the data I don t know Other No one 55% 52% 43% 38% 24% 10% 5% 4% 0% *Respondents could select more than one answer. In the majority of organisations (55% of respondents) the business unit that uses the data bears some responsibility for its accuracy and quality. Inevitably the IT department is also significant (52%) in this respect, as are the people who create the data (43%). Smaller proportions of respondents identify a data governance officer or team (38%), a compliance officer or team (24%), or governance committee (10%). Computing research paper sponsored by SAP 9

10 Data stewards The fact is, when it comes to data governance, everyone in an organisation, from the c-suite down, has a role. Everyone who touches enterprise data is accountable for ensuring that it remains protected, consistent, accurate, transparent and accessible. Governance should include the principle of stewardship, whereby the organisation entrusts the care of a resource to an individual or a group. Thus specific people do not own a company asset, such as enterprise data, but they can be held formally accountable for it. Governance should include the principle of stewardship, whereby the organisation entrusts the care of a resource to an individual or a group The responsibilities for data stewardship are commensurate with the levels of accountability in the organisation. Clearly the c-suite has stewardship responsibilities that are different from line-of-business managers and individual employees. A third of organisations in Computing s survey do not employ a data governance officer or compliance officer or designated data stewards (Fig. 6). Fig. 6 : Does your organisation employ a data governance officer or similar (e.g. compliance offer) and assigned data stewards? 3% 21% 18% 33% Neither role is employed Don t know Both data governance officer and data stewards Data governance officer only Data stewards only 25% 10 Computing research paper sponsored by SAP

11 Just over one in five (21%) of respondents organisations employ both a data governance officer and data stewards. Slightly fewer (18%) employ a data governance officer but not stewards, and just three percent have data stewards but no single senior officer who is responsible for data governance. A data governance officer without stewards could pose a problem. How is this individual to enforce data governance standards and procedures? What jurisdiction does he or she have over the creators and users of data to ensure the very thing for which he or she is held accountable is carried out? The job title or role of the data governance officer in the organisations that employ such an individual, reveals how companies regard the issues. It is as likely that the role will be fulfilled by someone with the words data governance in their job title as with the words compliance or risk. The role is also often encompassed as part of data protection or data security; senior IT roles, such as data architect, also figure. In a few organisations, responsibility for data governance lies with a legal role, such as the company secretary or deputy. Stakeholder group A data governance stakeholder group drawn from various parts of the organisation for example, line of business, operations and IT can play an important role in the data governance strategy by defining the scope of data governance, lining up the resources and ensuring management support and funding. This does not imply that these people are exclusively confined to this role, but they are clear as to what their responsibilities and accountabilities for data governance are. The stakeholder group should determine how data governance procedures will be enacted, how they are to be enforced, and endorse the standards with which data will be governed, the metrics by which the success of the strategy will be judged and continue to promote the value of data governance in the organisation. Not all the organisation s data needs to be governed in this way, but the stakeholder group should decide which data fields will yield the most business benefit from governance. Data governance experts should make recommendations on which enterprise data assets should be supported (and funded) by stakeholders. The stakeholder group can then institute the day-to-day stewardship processes to formalise governance accountabilities, tackling the most urgent or profitable requirements first. The group can also ensure that the IT department fulfils a coordinating role in information governance. Computing research paper sponsored by SAP 11

12 Governance programme Despite the acknowledgement among companies that their data is valuable, a board-sponsored data governance programme, with data governance officers and substantial investments in people, technology, and process is a rarity just 17 percent of respondents have such a programme in place (Fig. 7). Fig. 7 : Which of the following statements best describes data governance in your organisation? We have no data governance programme 28% We have a soft data governance programme, consisting mainly of a steering committee and a set of lightweight policies 20% We have a corporate-wide data governance programme, including data stewards and ownership, but it is either in its infancy or does not have a full mandate 18% We have a board-sponsored data governance programme, with data governance officers and substantial investments in people, technology, and process 17% Don t know 15% Other 2% A similar proportion (18%) has a corporate-wide data governance programme, including data stewards and ownership, but it is either in its infancy or does not have a full mandate. More popular (20%) is a soft data governance programme, consisting mainly of a steering committee and a set of lightweight policies. The largest proportion (28%) has no data governance programme in place. Health check The maturity of an organisation s approach to data can be seen in the governance procedures it applies to its data domains. The Computing survey reveals a mixed picture. Just three percent of respondents have a full data quality shared service, and one in ten have a full data quality platform and clean data across multiple domains using consistent rules (Fig. 8). 12 Computing research paper sponsored by SAP

13 Fig. 8 : Which of the following statements best describes data quality in your organisation? 16% 10% 5% 3% 42% We actively cleanse data, but only in a single domain or systems We do little or no active data quality control Don t know We have a full data quality platform, and clean data across multiple domains using consistent rules Other (please specify) 24% We have a full data quality shared service/coe Less than half the respondents (42%) actively cleanse data, but only in a single domain or systems, and nearly a quarter (24%) confesses to doing little or no active data quality control. One anecdotal response says we do reactive data cleansing in response to incidents. What about the frequency with which data is cleaned or de-duplicated (Fig. 9)? Fig. 9 : Are your organisation s primary data repositories ever cleaned up or de-duplicated? 1% 8% As the data governance policy dictates 12% 32% When we have time Annually or thereabouts 12% The data pertinent to each project is cleaned at the commencement of the project We ve been meaning to get around to it Never 13% 22% We did it once Computing research paper sponsored by SAP 13

14 Twenty-two percent say they clean data when we have time which in the modern business environment is not a frequent fixture on anyone s calendar; 12 percent are have been meaning to get around to it ; eight percent who have never cleaned data; and one percent who did it once. When it comes to master data management (MDM), only three percent of respondents has a full decentralised multi-domain MDM programme and full central management and authoring. The same proportion has a full decentralised multi-domain MDM programme. Conclusion The quality of data available to an organisation for business operations and decisions fundamentally impacts the health of the business. Poor quality data can have many repercussions, from an erosion of productivity and slow business decision-making through to more serious consequences, such as failed projects, reputational damage and failure to comply with legally enforced regulations. The majority of respondents to Computing s survey say that their organisation puts a high value on business data, but around one in four confesses to doing little or no active data quality control. Effective data governance requires a management process that includes people, processes, policies, and metrics, spanning the life cycle of information assets from creation and capture through to archiving and deletion, as well as quality, security, accessibility and disclosure. Not all the organisation s data needs to be governed: the first step to data governance maturity is to separate the news from the noise, identify their critical enterprise data, and ensure that it is governed with the same rigour as other balance-sheet assets, such as people, cash, buildings and machinery. Data governance should be based on direct business need, such as accuracy of financial data, with stakeholder groups deciding which data domains will yield the most business benefit from governance, endorsing the standards with which data will be governed, and the metrics by which the success of the strategy will be judged. As the data governance strategy matures, it can be rolled out to other domains. With data volumes increasing year on year, those organisations that can put a proper governance regime in place will inevitably be those that emerge ahead of the pack. In the best-run organisations, data governance is a central plank in all decisions and operations involving data. Is this the case in your organisation? Like cash, data won t look after itself. A proactive approach is needed to both initiate proper governance and ensure it continues into the future. This should include: Fostering a view within the organisation that enterprise data is central to the organisation s value Centralisation and consolidation of data into a multi-domain data warehouse Ensuring all data protection laws and industry best practice are well understood and complied with by all relevant employees Setting up a comprehensive data governance strategy to include clear lines of responsibility, Creating roles to implement the strategy, including a stakeholder group, designated data stewards and a senior executive with ultimate responsibility for data quality Deployment of rules-based systems and technology to actively clean, de-duplicate and marshal the data and to ensure all new data is compliant. 14 Computing research paper sponsored by SAP

15 About the sponsor, SAP In today s information-saturated environment, businesses need to maximise the value of enterprise information to improve efficiency and effectiveness. But managing information and processes across interconnected applications and systems is a complex endeavour. With information governance initiatives supported by SAP solutions, you can make the most of your enterprise information, empower people across the business with information access and ownership, improve information transparency, and enable collaboration. You can help ensure compliance with retention laws and regulations. You can govern information in process to monitor and optimise quality and consistency. You can improve information quality and gain rich business insights through the power of trusted enterprise information and help ensure quality and consistency throughout the data lifecycle. To learn more about how SAP solutions for enterprise information management can support your governance initiatives visit us online at Contact SAP: Visit: Computing research paper sponsored by SAP 15