No one searches for information just for the fun of it. Behind the process of searching is a requirement to find the information needed to make a decision. How often have you had to make a decision without feeling totally confident that you had found all the relevant information you needed?
On June 10, 2000, the London Millennium Footbridge across the River Thames was opened. Shortly thereafter, it was obvious there was a problem. It became known as the “Wobbly Bridge” because it started to sway as pedestrians began using it. Within a couple of days, it was closed down, reopening in 2002 with a new damping system. In the course of investigating the issue, it was found that this was a known problem. The paper describing it had been published in the journal Earthquake Engineering and Structural Dynamics in 1993, but the designers of the bridge were not aware of it.
What might be the outcome for your organization in a similar situation?
Reading this book and taking appropriate action will help everyone in your organization make better decisions. Some of these decisions will be about implementing and managing search applications. Other decisions will have a direct impact on the organization. Enterprise search is a decision support application. It is a business-critical application that needs to be planned, resourced, and managed at a level commensurate with the decisions that it is supporting. That is why the strapline for this book is “Enhancing Business Performance.” It is easy to devote a lot of effort to analyzing search logs and producing visually attractive dashboards showing an increase in use of a search application. The increase is almost certainly due to the fact that the volume of information in the organization is increasing and so each individual item becomes more difficult to find.
There are only two important success metrics for search. The first is that users trust it to find all the information they need. The second is that it makes an impact on business performance. The objective of this book is to help your organization improve both the level of trust and the impact on performance.
Every day people make the news headlines because they made the wrong decision. The financial meltdown of 2008 was arguably an information problem. Loans had been made to people purchasing homes without adequate security. The pressures of making sales targets led to an inadequate review of the circumstances of the people asking for loans, and senior managers in the banks had no information about the scale of the problem.
Once upon a time, you could at least walk into your office in the morning and feel reasonably certain about the decisions you would need to make. With the arrival of 24/7 mobile access, reductions in staff, and difficult economic and market conditions, you may well get a call at any time during the day from colleagues just about to walk in to a prospective client and have just realized that they did not have a critical piece of information about the client or the proposal they were making.
That puts the pressure on to find information that could have a very positive impact on the bottom line. Fortunately, your organization has invested in an enterprise search application! You enter a few keywords into the search box, sit back, and within a few seconds discover that there either seems to be no information at all on the query you have made, or you find that there are over 3,000 documents and you only have a few minutes to hunt through them to provide a response.
When we are dealing with decisions that are based on some standard business processes, such as setting up a project or writing a monthly report, we often rely on browsing through the information architecture of an intranet, shared file collection, or a document management system to find the information we need.
In 2014, PwC published a global survey of decision making in organizations with a particular focus on decisions that could change the business direction of the organization and increase revenues by millions of dollars. The survey was conducted among over 1,000 senior executives around the world. It showed that almost half of these executives make a “big” decision every month and a further third make a “big” decision every three months. The timing of these decisions is almost always opportunistic and not to a specific schedule.
These “big” decisions are supported by many other employees in the organization making decisions that will eventually feed into the briefing given to a senior executive.
Search becomes critical when there is time pressure and a need for an immediate solution. When using the Web for information, we may well be unaware about how often we use Google or Bing to find a website, then use the search function on the website itself, and finally go back to Google or Bing to check that we have not missed out on a better deal or a more recent source of information.
The situation inside an organization is just the same. We expect it to be as easy to use as Google and at least as effective, providing us with the information we need on the first page of results. Anything less, and the search application is regarded as a failure. Google and Bing have huge scale, and an immense amount of development has gone into providing search experiences across the Web. Searching for information inside a single organization seems like it should be easier, but in reality it is much more challenging.
Many companies attach asset numbers to all of their property, be it a wastepaper bin or a complex machine tool. All those assets are logged in a database and their residual financial value will be given on the balance sheet of the company. The balance sheet will also show the financial assets of the business.
No matter how hard you look, there will be two corporate assets missing from the balance sheet. One of these is the employees, though at least there will be a record in the annual report of how many employees there are, possibly categorized by location or gender. But what about the information assets of the business, and the knowledge assets possessed by each employee? International accounting standards do not allow for information to be capitalized as an asset because there is no definitive way of calculating its value. The value of a piece of information is unique to an individual at a particular point in time. In search terms, it has a different relevance (I’ll have much more to say about relevance later in this book).
In addition to recording every physical asset they own, companies entrust employees to manage these assets and make decisions about when and how they should be replaced or upgraded. In most companies, no one owns information as a corporate asset, even though there may be someone with the title of Chief Information Officer. There is now a growing concern among senior managers about the sheer scale of corporate information resources with the arrival of the concept of Big Data. With hundreds of applications being used each day inside even a modest-sized company, the amount of data and information that is being collected is often poorly understood. Worse, because of the low cost of storage, nothing is ever deleted. As a result, the rate of growth is a combination of new information and old information, with the assumption that all information has a value (of course, that value can only be realized if the information can be found!).
The term unstructured information is widely used to describe documents, emails, blogs, and other text information, and more recently, rich media applications. In fact, this information does have a structure, in that there is usually a title, an author, a date, and perhaps section headings and tables. The term came into use to distinguish these categories of information from structured databases in which data is stored in defined fields such as Address Line1, Address Line 2, Town, and so on. For many years, the UK search vendor Autonomy emphasized the fact that unstructured information represented 80% of the total information assets of the organization. No evidence was ever presented for this assertion, which seemed to be based solely on the Pareto principle. More important, there is no relationship between volume and value.
Until recently, enterprise search was used primarily to search unstructured text, and it therefore needed to be able to cope with the issues of language and semantics. For example, consider these two sentences:
Noah loaded boxes into the van.
Noah loaded the van with boxes.
In the case of the first sentence, the number of boxes could be any number of two or more. In the second sentence, there is the implicit message that the van was totally full of boxes, though we cannot be sure.
The textual differences between the sentences are very small but semantically very important. In almost every conversation we have, we are constantly checking whether we have fully understood what others are saying, perhaps asking for clarification from time to time. In the case of a document that might have been written several years ago, we cannot have this type of conversation, and yet we expect a search engine to be able to read and understand the document, and then be able to say with certainty that the document contains information that is relevant to the search we have carried out and list it in the top few results.
Search software is both very smart and very stupid. Search applications can index information at an amazing speed and deliver the results of a search within a second or so of being asked a query. However, no search application yet built can distinguish between high-quality and low-quality information. It treats both just the same. When users criticize a search application, they make the assumption that the problem lies with the technology. Almost always the problem lies with people, not technology.
Although employees may spend much of the day in creating information, there is usually very little guidance on the process of information creation and curation. Ensuring that every content item has an informative title, a date, and author will make a difference, even more so if there is a review process that at least on a yearly basis ensures that the item is still fit for purpose. This is a particular problem when companies have merged. A search on a topic may well find information dating back to before the merger, leaving the employee to make a judgement on whether the information is still valid.
In the second instance, it is a lack of people to create an adequate depth of experience in the search support team.
Without addressing these people issues, there is no point in investing in a new search application. All the new technology will do is highlight a lack of content quality and management support even more quickly than in the past.
It is only over the last few years that surveys have been undertaken about the challenges of finding information inside organizations. In this respect, 2014 was a very good year, as the Association for Information and Image Management (AIIM) conducted its first ever survey of search implementation and Findwise published its third Enterprise Search and Findability Survey. The AIIM respondents were mainly based in North America, and the Findwise respondents were mainly European organizations. Although the questions asked in the two surveys were slightly different, a very consistent picture emerged from these and other surveys.
Quotes from some of these studies include:
For 71% of the organizations polled search is vital or essential, yet only 18% have cross-repository search capabilities. The IT department takes responsibility for search in 52% of organizations but only 25% feel that it should be so. 38% have not tuned or optimized their search tool at all, and half of the responding organizations allocate less than half an FTE to support search applications.
Better decision-making and faster customer service are the top benefits from improved search tools. Only 14% were required to make a financial business case for search investment. 42% consider that they have achieved pay-back from their investment in search tools within 12 months or less, and 62% achieved pay-back in 18 months.
AIIM, “Search and Discovery—Exploiting Knowledge, Minimizing Risk” (2014)
86% of respondents reported that it was either very important or important to improve the ability to find information in their organizations but only 38% had a strategy for doing so. 48% said that employees found that it was very difficult or difficult to find the information they needed and less than 5% reported that their employees were very satisfied with the search applications.
Findwise, “Enterprise Search and Findability Survey 2014”
Only 23% of organizations have enterprise-wide search and a further 16% have a more limited level of implementation. 22% have no plans to implement enterprise-wide search. Only 11% are very satisfied with search, a figure that has changed little over the last five years.
NetStrategyJMC, “The Digital Workplace” (2014)
The situation seems to be getting worse, not better. The 2015 edition of the Digital Workplace Trends Report showed that the percentage of respondents who reported that they were either very satisfied or even moderately satisfied was lower than in the 2014 edition, and was in fact at the lowest level since the survey was launched in 2009. The rate of growth of information stored digitally in an organization seems to be overwhelming the capability of the organization to provide adequate search solutions.
The headline summary of all these surveys is that information is becoming more important, more difficult to find, and yet there is an inadequate level of investment in improving information quality and the resources to enhance search effectiveness.
An enterprise search application enables employees to find all the information that the company possesses without the need to know where the information is stored.
The position I take in this book is that enterprise search is not about selecting and installing a single search application that will index every item of information and data owned by the organization.
This is my definition:
Enterprise search is a managed search environment that enables employees to find information they can rely on in making decisions that will achieve organizational and personal objectives.
Many companies already have one or more search applications, either operating as a discrete search application or embedded into another enterprise application. Trying to replace all of these with one HAL-like enterprise search application is not a realistic strategy. One of the buzzwords in enterprise search is federated search, which is an attempt to provide one single search box linked to one single search application that has an index of every single item of information and data that the organization has created.
The sales pitch is that a single enterprise search application can break down information silos. The issue is whether that will have value. In many organizations, the information silos reflect different areas of business or technology. Searching across all these silos could result in having to work through a very long list of search results, many of which seem to be highly relevant but in fact are highly relevant to a particular silo. There can also be some significant access permission management problems that need to be solved. This is not to say that federated search is never an effective approach, but the decision to implement federated search needs very careful consideration of both the benefits and risks. These are discussed in Chapter 4.
The downside of this definition is that it excludes search applications on the corporate website. These should certainly be included in any enterprise strategy, even though they may be owned by corporate communications and use a hosted search application. The skills needed to support website search are the same as those for internal enterprise search, and there will be benefits from taking an integrated approach to their management.
Information retrieval deals with the representation, storage, organization of, and access to information items such as documents, web pages, online catalogs, structured and semi-structured records, and multimedia objects.
There are two different perspectives of information retrieval research: the first considers the computer technology of information retrieval, such as ways of building efficient indexes and finding ways to handle multiple languages; and the second is user-based, looking at search user interfaces and how people go about constructing search queries. Although there are some very distinguished university departments of information science around the world (many now called information schools, or iSchools), few teach information retrieval in any depth as an undergraduate course, and this means that the annual output of graduates with skills in search implementation is very low indeed. Computer science departments, of which there are many more, also pay little attention to the science and technology of enterprise search, even though many of the major IT vendors, such as IBM, Oracle, HP, and Microsoft, have a long history of carrying out information retrieval research, as, of course, does Google.
The scale of the science behind search can be seen in the fact that the standard textbook on the subject, Modern Information Retrieval (Addison-Wesley) by Ricardo Baeza-Yates and Berthier Ribeiro-Neto, is 700+ pages in length and includes a bibliography of nearly 2,000 references. Marti Hearst’s Search User Interfaces (Cambridge University Press) is 300+ pages and has around 500 references in its bibliography to research papers on user interface design. Elasticsearch: The Definitive Guide is 700+ pages.
Sadly, there seems to be a gulf between the information retrieval community and the enterprise search community. Some information retrieval conferences do include sessions where papers from the commercial search world are presented. For some years, there was an Enterprise Search Summit in New York, but this has been discontinued. There is an Enterprise Search Europe conference where the emphasis is very much on enterprise search implementation and management. There are signs that the situation is now starting to change and in the future, much closer ties are likely to develop between the information retrieval community and search software vendors and users. A good example is the annual Search Solutions conference organized in London by the Information Retrieval Specialist Group of the British Computer Society. Hopefully the rapid adoption of open source search solutions will catalyze the launch of new conferences in the next few years.
Site search of a public website
Search engine optimization
Internet search and search engine optimization fall outside of the scope of this book, but information retrieval certainly does not, and yet is probably a totally unfamiliar topic to most search managers.
The term was first used by Calvin Mooers, a pioneer in the early history of the development of computer technology in the 1950s and 1960s. Mooers made the point that one of the challenges of information retrieval is that the person contributing the information to a system has no idea of when the information will be found by a searcher and what it will be used for.
An information retrieval system will tend not to be used whenever it is more painful and troublesome for a customer to have information than for him not to have it.
Where an information retrieval system tends not to be used, a more capable information retrieval system may tend to be used even less.
Mooers’s law is a reflection that an information retrieval system will not be used if in doing so there is a chance that the information found may put the user to some inconvenience. In 1989, J. Michael Pemberton rewrote this law as follows:
The more difficult and time consuming it is for a customer to use an information system, the less likely it is that he will use that information system.
This is the form that tends to be quoted today.
The corollary needs a brief explanation. What Mooers realized was that if users have a poor experience with an information retrieval system, they are unlikely to try again in another system even if they are told it will produce better results. They are more likely to send an email or call someone on the telephone.
Although much of the early work on information retrieval algorithms was undertaken in the United States, Stephen Robertson and Karen Sparck Jones, working at Microsoft Research, Cambridge, and the University of Cambridge also developed some important elements of current search applications. It was in 1972 that Sparck Jones published a paper on what would become known as inverse document frequency (IDF). Her intuition was that a search term that occurs in many documents is not a good discriminator, and should be given less weight in assessing relevance than one that occurs in a few documents. IDF was a heuristic implementation of this intuition and was a very significant development in information retrieval. In the 1990s, Robertson, with Sheila Walker, went on to develop a weighting model called BM25, which remains a core element of most search applications, including Microsoft SharePoint. One of the best introductions to BM25 can be found on the Microsoft TechNet site, along with a good overview of a range of other ranking models.
Gaining some understanding, even without a full appreciation of the mathematics, is important to be able to appreciate the fundamental principles of information retrieval and the challenges of being able to rank results in order of relevance.
Information science is just one of the sciences behind search—mathematical probability, computational linguistics, and computer science also come into play. Around the world there are probably several hundred academic departments offering courses in information science, of which information retrieval is a core topic. In particular, there are nearly 60 iSchools specializing in information science and information retrieval. However, research in information retrieval is often based on well-defined sets of documents and other content items.
Search came into prominence with the advent of the web search services in the 1990s, notably Alta Vista, Google, Microsoft, and Yahoo!. However, the history of search technology goes back much further than this. Arguably, the story starts with Douglas Engelbart, a remarkable electrical engineer whose main claim to fame is that he invented the computer mouse, which is now a standard control device for personal computers. In 1959, Engelbart started up the Augmented Human Intellect Program at the Stanford Research Institute (SRI) in Menlo Park, California. One of his research students was Charles Bourne, who worked on whether it would be possible to transform the batch search retrieval technology developed in the 1950s into a service based on a large mainframe computer that users could connect to over a network.
By 1963, SRI was able to demonstrate the first “online” information retrieval service using a cathode ray tube (CRT) device to interact with the computer. It is worth remembering that the computers being used for this service had 64 K of core memory. Even at this early stage of development, the facility to cope with spelling variants was implemented in the software. Other pioneers included System Development Corporation, Massachusetts Institute of Technology, and Lockheed. The main focus of these online systems was to provide researchers with access to large files of abstracts of scientific literature to support research into space technology and other large-scale scientific and engineering projects.
These services were only able to search short text documents, such as abstracts of scientific papers. In the late 1960s, two new areas of opportunity arose, which prompted work into how to search the full text of documents. One was to support the work of lawyers who needed to search through case reports to find precedents. The second was also connected to the legal profession, and arose from the US Department of Justice deciding to break up what it regarded as monopolies in the computer industry (targeting IBM) and later the telecommunications industry, where AT&T was the target. These actions led IBM in particular to make a massive investment into full-text search, which by 1969 led to the development of STAIRS (Storage and Information Retrieval System), which was subsequently released as a commercial IBM application. This was the first enterprise search application and remained in the IBM product catalog until the early 1990s.
However, it is important to keep in mind that not all the developments were taking place in the United States. For example, a team at the United Kingdom Atomic Energy Authority took the lead in using mini-computers to support online services in the mid-1970s.
The problem with STAIRS was that at least in its initial versions it could only search for words that appeared in the document. What researchers wanted was to find information about concepts that were not present as words in a document, especially if they were working for the security services. Dynamite can be used for mining but also to make a bomb, and they needed a search system that would present results that included dynamite to a search query on bomb making. One of the innovators in the mid-1980s in developing concept searching was Advanced Decision Systems (ADS). Verity was the name of the company that was spun off from ADS to bid (successfully) on a US Department of Defense/US Air Force project. A feature of the Verity Query Language was the capability to weight topics against a taxonomy tree. Verity was also able to offer real-time indexing. Verity became one of the most innovative companies in enterprise search, and in 2003, it acquired the enterprise search business of Inktomi Corp., relaunching the application as Ultraseek. Then, in 2005, Verity was acquired by the UK-based search company Autonomy. The story of the enterprise search industry continues in Chapter 5.
Earlier in this chapter, I remarked on how in conversations we are constantly engaged in a dialog to ensure that we understand what the people we are talking with are trying to convey. It is very important to understand that search is a dialog. We tend to see search as a “first strike” application; just putting a search term into Google or Bing will provide all we need in the first page of results. The reality is that Google sometimes needs to correct our spelling mistakes or prompt us with “did you mean” suggestions. On the page, there are filters that we can use to narrow down our search, and on public search sites, there is paid-for advertising that also offers solutions to our problems.
We often go into a large department store to find a birthday present, and yet I have never come across a store with a Birthday Present Department. We may look at the store directory (the information architecture) for ideas, but if we are in a hurry, we may also go to the Information Desk (the search box) for advice. There we will be asked the age and gender of the person for whom we are buying a present, and what their interests are, in order to suggest one or more specific departments we might wish to explore. Once in the Sporting Goods section, we may have another conversation with a floor manager about which is the best set of soccer goalkeeping gloves.
The importance of this conversation is that it is an example of what is often referred to as exploratory search. There is a tendency to focus the search team’s efforts on optimizing the delivery of specific documents in search results (or as best bets) because the information architecture of the intranet or document management system is not optimal. Exploratory search, where there is a strong requirement for the search system to support a dialog with the user, is much more challenging to develop, test and implement. Search really becomes useful when it makes it possible to go on a journey through the information resources of an organization, starting with perhaps a vague idea of the initial query and progressively refining the search query, or even starting all over again, on the basis of the information found.
The challenge with search, as is the case for the staff of the Information Desk, is that all users are different, with their own individual perceptions of what would make a good birthday present and what would represent value for money. In the business environment, the challenge is to find a way of meeting the individual expectations of each staff member without having to provide individual search applications. Indeed, the aim is to make them think that it does actually work just the way they want it to.
For over a decade, I have been providing consulting services in management of intranets, and one of the most common issues is who should be taking responsibility for intranet development and operation in a company. An intranet, like search, is a very high-touch application, with most, if not all, employees using the intranet every day. The information on an intranet will be authored by most departments in the company, but clearly the people managing the application need to report to a manager who has the budget to support the intranet. The end result is that an intranet can be owned by corporate communications, HR, IT, or even marketing on the basis that an intranet is just another website.
In the final analysis, it should not matter who owns search, and the same situation applies to an intranet. Both should be managed within an overall information management policy and an information management strategy, but very rarely are. Some years ago, I went to run an intranet workshop for a major UK organization for which the effective management of information was probably its main competitive advantage. When I arrived, I noticed that all the cars were reverse parked, and it looked very neat and tidy. It transpired that the organization was concerned about safety and at the end of the day did not want staff reversing out of a parking space and either crashing into another car or staff walking to their cars. The parking policy was published on the intranet and at the reception desk, and it was made clear that a very dim view would be taken if the policy was not followed.
However, this organization did not have any policies about the management of information, so almost every document was written in a different format, often with no owner or even a date on the document. The quality of the search experience is directly related to the quality of the content. The old adage of garbage in, garbage out (GIGO) applies to search more than any other application. Someone has to take responsibility for information quality within an overall information management strategy. This is ideally written around an information life cycle, of which the following is just one example. The use of the term document is just a convenience and could be any item of information, from a personal profile to a video file.
The following is an example of an information life cycle:
This is the process of creating documents in a way that enables the document to progress through the stages of the information life cycle. These might include establishing document categories, writing good titles, and adding metadata. There could also be a quality assurance process.
There are many places that documents can be stored, including local and shared drives, document management applications, Lotus Notes applications, and intranets. A set of criteria needs to be established so that employees know where documents should be stored so that they can be located and accessed by any employee with permission to do so.
One of the differences between website search and enterprise search is that only certain groups of employees are able to see specific items of information. In addition, in some sectors, governments impose export license controls that mean access to corporate information repositories while outside the country could be restricted. Managing access permissions is a challenge for search managers.
Information can be found by searching through repositories, browsing through folder structures and intranet navigation, and through alerting services such as wikis and blogs. Each has a role to play in the discovery process. The process can be facilitated by good usability and the design of intuitive lists.
The employees need to be able to feel confident in the quality of the information they are using, and that the information is valid for the particular use to which it will be put.
This is not the same as access authentication. There could be implications from Freedom of Information or Data Protection legislation on the use that can be made of the information.
The employee needs to be certain that the information can be shared internally, and if required, with third parties and with the public. Users of these documents have to be confident that the information they contain can be trusted to be reliable, and that if needed, the documents are available in a number of different languages.
As documents are shared, others may have views on the accuracy and value of the document. A system must be established for undertaking the review process, and if needed, creating a new version of the document. A possible decision could be that the document is disposed of to prevent inadvertent use at some time in the future.
Some documents will need to be retained in a secure environment for an agreed on period of time. Details of retention periods need to be established, and must take into account legal and regulatory requirements, as well as product and service lifetimes.
Disposal is the final stage of the information life cycle. At this point, the document has no further value to the company and can be deleted from all systems without any risk to the future integrity of the company.
If the discover phase is broken in any way, then the information that has been laboriously created and stored cannot be used and cannot be shared. It has become invisible to people who could benefit from it. If you take the view that much of an organization’s knowledge is also in a documented form, this knowledge also cannot be used and shared.
The review phase is also important, as it is this process that maintains the quality of the information, perhaps even enhancing it with additional metadata in the light of a change in business direction and/or a review of search logs and user requirements.
The biggest single challenge that any search manager faces is making a business case for a level of investment in search that is appropriate to the requirements of the company. The process of making a business case is covered in depth in Chapter 9, but for now, the following subsections will take a brief look at the many business benefits of good enterprise search.
Every day, most employees will have spent time on creating information—everything from writing a business plan, sending an email, or reporting on a visit to a customer. The process of creation may well be of the order of an hour a day, or 12% of the working year. If this information cannot be found and used by other employees, then that time has been wasted twice over, as other employees may have had to create the information all over again. There is also information from external sources, such as market research reports, that has been purchased and will have a company-wide value beyond the original purchaser.
At a time when business growth is static, finding new business opportunities is of the highest importance. When an opportunity does arise, the speed with which the company can find examples of relevant experience or size can be the difference between winning the business and being a poor second.
If a business opportunity arrived on your desk today, how quickly could you respond with a proposal that had low risk and a good financial margin? An enterprise search application can reduce the research time from days to hours, if not minutes, making the best use of staff expertise.
It is important not to focus just on information—knowledge must also be considered. Knowledge cannot be written down, as it is context specific and changes day by day as new knowledge is gained. Typically, companies have employee turnover rates of 10% a year. In a company with 5,000 employees, this means that, on average, every working day two people arrive at the company to build their careers and enable the company to meet its objectives.
How certain are you that these employees will be able to track down people with the expertise and experience they may need to make an immediate and effective contribution? Enterprise search can play an important role in finding them, though this is by no means as easy as many vendors would have you believe.
New employees want to make a positive contribution as quickly as possible. They do not have the time or the inclination to work through the navigation of the intranet or the folder structure in the document management system, nor do they know the names of people who might be useful to them as they begin work.
Employees taking on new roles and responsibilities will be in just the same position, but possibly with a greater need to get up to speed as the expectation will be that they know exactly where all the relevant information will be located. If only!
One of the most significant benefits of enterprise search is that once the deal has been done, employees in the acquired company need to have immediate access to the information resources and employee knowledge base of their new employer. In addition, the business case for the acquisition will have been based on the skills and knowledge that the acquired business will bring.
In those crucial early days, enterprise search can make a substantial contribution to the rapid and successful integration of the acquired company by quickly indexing the information resources of the acquired company.
Many of these employees will be working outside of the office, dealing with customers, prospects, and suppliers. They will need information as the meeting is taking place to confirm the details of a product or the name of a subject matter expert in the company.
Mobile users will use enterprise search on their smartphones or tablets to find information on a close-to-instantaneous basis and close the deal.
Routine tasks are rarely routine. New policies emerge and new forms are devised to capture information. Of course, what is a routine task for a long-serving employee is not routine for someone new to the company or the role. In both cases, there never seems to be enough time to complete the tasks.
Embedding search into a task can ensure that as the task is undertaken, the most recent information is presented to the employee by the enterprise search application working in the background as a search-based application.
All the evidence suggests that organizations are ill prepared for the rate of growth of information they are experiencing. Because information is not seen as a business asset, with an associated information management strategy, organizations have no view on the scale of the problem. As a result, no one is taking ownership of the problem because it’s not being perceived as such.
If information cannot be found, then the effort and investment in creating and storing it are wasted. The work may need to be duplicated (if there is time to do so) or a decision made on what can be found, even if it does not represent the best of what the organization has in terms not only of information but also of knowledge.
Seeing enterprise search as the quest for a single search application that can index all organizational information is not the solution. Enterprise search is about creating a managed search environment that enables employees to find the information they need to achieve organizational and/or personal objectives. There will be many different business cases that need to be addressed within this managed search environment, each contributing to the overall investment case.
At the end of this book, Appendix B sets out a core library of books and reports on information retrieval and enterprise search. Most of the individual chapters also include a “Further Reading” section listing out more specialized sources of information that have either been referred to in the chapter or will provide additional information and guidance.
A comprehensive list of books on search technology and implementation can be found at http://www.intranetfocus.com/enterprise-search/books-and-reports, and a list of blogs on search can be found at http://www.intranetfocus.com/enterprise-search/enterprise-search-blogs.
Association for Information and Image Management (AIIM), “Search and Discovery—Exploiting Knowledge, Minimizing Risk”, September 12, 2014.
Chun Wei Choo, “Information Culture and Organizational Effectiveness,” International Journal of Information Management 33 (2013): 775–779.
Paul H. Cleverley, Simon Burnett, and Laura Muir, “Exploratory Information Searching in the Enterprise: A Study of User Satisfaction and Task Performance,” Journal of the Association for Information Science and Technology. Published online in Wiley Online Library (wileyonlinelibrary.com, 2015). DOI: 10.1002/asi.23595
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