IT security can be expensive. For years, large enterprises have used IT service providers for security and data storage as well as for technical support. Smaller businesses often ran their security in-house, wary of the high cost of outsourcing data operations and security to an outside IT management company.
The Rise of the Cloud
The advent of cloud computing for data quality and other data operations has allowed small and medium-sized companies —the majority of businesses worldwide— to take advantage of massive distributed processing power to achieve rapid results, greater scalability and impressive cost savings. The service standards once available only from expensive IT consultancy and management firms are now available on demand to smaller businesses around the world through the cloud.
Small businesses are not the only ones migrating to the cloud. IT management service providers have brought many large enterprise clients with them to the cloud as the standard IT service range expands to include cloud storage and software-as-a-service offerings. The convenience of storing, accessing and processing data on a service provider’s servers via an Internet connection is appealing to most businesses. However, many are concerned about issues of cloud security, and enterprise needs security to survive.
A Question of Security
With the rise of big data, increasing rates of cybercrime, and tightening data protection and privacy legislation, many businesses of all sizes face a dilemma: more data than ever is at greater risk than ever, yet it must be secured more thoroughly than ever.
Security was, until recently, a deal-breaker for some companies considering cloud services. Survey results from technology research group IDC show that in 2008, security was the main concern that prevented adoption of cloud services for business. In 2011, however, less than half the responding companies cited security worries as their leading concern, and the number of businesses already using cloud computing services is rising swiftly.
Safer in the Cloud
By outsourcing to the cloud not only data security and storage, but also the processing locations of the data, vital files may be doubly protected from hacking. Cloud computing security features may include firewalls; high strength data encryption; customisable user privileges; activity logs and auditing; anomalous activity notifications; and secured collaborative workspaces.
Even entry-level cloud computing services, and those not explicitly security-related, can improve a business’ overall security because cloud services are maintained and secured by the provider rather than relying on the user to keep antivirus programs up-to-date and apply recommended software security patches. Jim Reavis of Cloud Security Alliance has commented that for small and medium businesses, migrating to the cloud “ends up being almost in all cases a security upgrade.”
If you’re curious about cloud security and how Match2Lists helps to protect your data, feel free to get in touch or take a look at our security statement. You can also take a free trial of Match2Lists to see how it works for you.
Happy matching!
What happens to businesses when data quality is low? Can you ‘muddle through’ a data quality disaster and come out the other side still firing on all cylinders?
Bad Data = Big Costs
A recent report from Artemis Ventures indicated that poor data quality costs the United States economy roughly $3.1 trillion per year. To provide some perspective on this unimaginably large figure, that’s twice the size of the US Federal deficit. An estimate from the US Insurance Data Management Association puts the cost of poor quality data at 15% to 20% of corporations’ operating revenue.
Meanwhile in Australia, David Howard-Jones of management consultancy Oliver Wyman confirms a similar trend. Speaking at an Institute of Actuaries of Australia conference, he said, “Overall estimates of the costs of poor data quality are 15 to 25 percent of operating profits for insurers and potentially even more for large banking groups.”
Low Quality = Low Efficiency
Even more jawdropping than the cost of poor data quality is the typical lack of action to improve it. The flawed data may be easily corrected with a cloud data quality tool like Match2Lists, but flawed data quality systems are often allowed to continue producing data that could have been better. And that’s when the wider effects of low data quality come into play: where there are data quality issues, the data is less trusted. Decisions become overly conservative and opportunities are missed due to lack of timely, reliable, usable data.
Failure to attend to the sources of data quality underperformance leads to unmanaged workflow friction that can slow a business’ operations and reduce their effectiveness. In turn, revenue and growth will be limited, and the economy as a whole will be carrying one more data dead-weight.
Hollis Tibbetts, managing director and principal analyst at Artemis Ventures, commented, “About half of IT executives consistently agree that data quality and data consistency is one of the biggest roadblocks to them getting full value from their data, yet consistently organizations fail to address this issue.”
So, what now?
1. Get Clean
Use Match2Lists to compare and match data, remove duplicates from your data, and merge lists of data without introducing new duplicates. It’s swift, smart and simple.
2. Stay Clean
Keep working. Data quality isn’t a one-off task or even a static goal to work towards; it’s a continuously evolving set of strategies that require ongoing contemplation and implementation. Evaluate your data and processes regularly, and fix any quality issues that you find.
3. Fix The Plumbing
Look upstream. Look downstream. Where is your data coming from, and how does it reach its users? What happens to it along the way? Now look again. Did you answer those questions by observing, or did you make assumptions? What really happens to your company’s data may not be what you intended or what you anticipated. Identify the problem points and involve the relevant teams in developing solutions.
Don’t leave poor data quality unmanaged. Take a free Match2Lists trial to check your data for duplicates and inconsistencies now.
Happy matching!
There are some common data quality misconceptions that, if they become entrenched in an organisation, can lead to a costly data downfall. If data quality is misunderstood, then quality suffers and the efficiency of every department from IT to PR takes a hit. That means less return on the investment you’ve made in obtaining and maintaining all that data!
Take a look at this quick list and see if your business has fallen for an expensive data fairytale:
1. Data quality is all about bad data that “got into” your systems
If you do have inaccurate or untidy data, we can help you get clean and stay clean. Sign up for a free trial of Match2Lists’ cloud data quality tools to match, deduplicate and merge your datasets.
Incorrect data certainly do pose a data quality problem, but data quality is much, much broader than that. What about data that can’t be accessed and analysed with ease, or data that is only consistent within its own little (or large) silo? What about data that is highy accurate and easily available on the whole, but not reported in a format that includes all the relevant data points each user needs?
2. You can solve all your data quality issues by fixing the bad data
Data quality is an ongoing process, not a target. Identifying and improving problem points throughout the process will yield far greater rewards than merely identifying and improving inaccuracies in the data itself. Without an agreed organisation-wide data quality policy and strategy, bad data will be the least of your concerns.
3. Data quality is too complicated for normal people
In fact, the essential principles of data quality are relatively simple. Everything is focused on trying to make your data as accurate, relevant, consistent, comprehensive and timely as possible. With cloud data quality tools like Match2Lists, anyone can swiftly match, merge and dedupe datasets on demand. You don’t need to know how it works, unless you want to.
Some of us might like to debate the finer points of fuzzy matching algorithms, master data management, and business rules. But Match2Lists is designed to make improving the quality of your data swift, smart and simple.
4. Truth is Data; Data, Truth
That’s beautiful, but it isn’t true. Truth, though a metaphysical concept, has to be true to real life when we're talking about data quality. But the real world is out there, and the data is inside your organisation. You can get awfully close to some pieces of truth with very good data and even better analysis and interpretation. However, the data in your hand are never quite the same as the facts in the field.
5. It’s all someone else’s fault
Please, don’t blame IT. Don’t blame the data operations team. Don’t blame anybody, it’s a waste of your time. Start here instead.
Data quality is a company-wide endeavour. There’s a growing trend for business data end users to process and analyse their own data, rather than receiving ready-made reports from a data analyst. Therefore everyone who creates, handles, oversees or uses the data needs to know their data quality aims, and the business processes governing and applying the data must be designed to maintain data quality in all its aspects.
For a swift, smart simple solution to some of your data quality issues, see what Match2Lists can do. Would you like to compare and match datasets, merge your data or remove duplicates from data? We can do that for you.
Happy matching!
We love the smell of feedback in the morning! Every time a Match2Lists user lets us know what they think of our cloud-based data quality tools, every single person on the Match2Lists team wants to hear all about it. So we were very excited to see some of our favourite data superstars testing out Match2Lists and sharing their opinions.
Match2Lists Users Say…
Data Quality and Master Data Management expert Henrik Liliendahl Sørensen kindly tweeted that he was “impressed by the features and user interface on the SaaS Data Matching tool from @Match2Lists.” We’re honoured to receive such approval from one of the leading figures of the international Data Quality & MDM community.
Information Management consultant Rohin Bhargava also devoted a blog post to reviewing Match2Lists, giving us some excellent constructive criticism and making us feel like proud parents! He writes, “All in all I was very impressed with the tool… you really have a winner.” It’s great to hear that users agree on one thing: Match2Lists works.
It’s awesome to hear the opinions of Match2Lists users, from data management experts to home business owners. Thanks for checking us out, and thanks for your comments! Every bit of feedback we receive is genuinely appreciated.
What we’d really like to know now is… what do you think of Match2Lists?
Take a free trial now — it only takes a minute to sign up, and you don’t need a credit card.
Then let us know your opinions and ideas: What did you like? Was there anything that you disliked? How can we make Match2Lists serve your needs even better?
Where to Talk To Us
You can contact us online any time, leave us a comment right here on the blog, or follow @Match2Lists on Twitter.
We’re looking forward to hearing from you – happy matching!
We read the latest Gartner Magic Quadrant for Data Quality Tools with great interest as always, seeing which data quality tool providers have been identified as leaders, challengers, niche players or visionaries. Gartner’s Magic Quadrants provide an excellent overview of the field for decision makers; however, as a recent post by Liliendahl on Data Quality pointed out, the majority of the world’s businesses do not use a Magic Quadrant solution for data quality.
Why no Gartner Magic?
Why? Simply put, because Gartner only include in the Magic Quadrant solutions that match their criteria. Match2Lists, for example, performs 3 essential tasks: data matching, merging lists and resolving duplicates in data. This makes us too specialised a tool for inclusion in the Magic Quadrant for Data Quality Tools, but an excellent option for anyone with data matching and data quality needs.
Let’s take a look at the key points made by the authors of the 2011 Gartner Magic Quadrant for Data Quality Tools. Here’s what you need to know:
High Demand for Data Quality
Business intelligence (BI) and master data management (MDM) require a constant high level of data quality activity, creating ever-increasing demand for tools to ease this workload and reduce the lead time on data quality projects. The sharp rise in the number of information governance initiatives implemented in the last year has driven further demand for data quality tools.
Uniting Data Quality, Integration and Management
Data quality tools now incorporate ever more data integration and master data management features, as businesses seek to streamline their data quality processes. Tools that can merge data intelligently and remove duplicates from data without sacrificing any useful information will therefore be increasingly popular in all industries.
Data Quality Tools for Data End Users
Gartner cautions organisations evaluating data quality solutions to “consider not only the functional capabilities… but also the degree to which this functionality can be readily understood, managed and leveraged by business resources rather than IT.” Intuitive interfaces and data visualisations allow the data end user to achieve success without the resource drain of regular in-house IT support.
New Approaches to Pricing and Delivery
Both interest in, and actual deployment of, offsite models grew rapidly in the last year. More organisations now seek hosted, SaaS and cloud-based data quality tools that provide the specific desired data quality capabilities on demand. Over 20% of the organisations surveyed used some sort of “as a service” solution alongside (or instead of) traditional software with onsite installation. This proportion is expected to grow as cloud computing solutions apply pay-per-use and pay-on-results models to their pricing.
Here at Match2Lists we’re very pleased to see that other data quality providers are beginning to offer cloud based solutions. And we designed our tools from the very beginning to appeal to both the data end user and the data specialist, so it’s great to hear that more data quality software developers are following suit. Here’s to another great year in data quality!
Source: Gartner (July 2011)
Today I read a very interesting post from Ken O’Connor on the concept of “an undertaking-wide common understanding of data quality”. Does your organisation have one of those? Are you sure?
For the first time, this has become a requirement for many organisations. Insurance organisations must now prove to industry regulator Solvency II that their calculations are based on the most complete, relevant and accurate data possible and that everyone involved is working from the same data quality textbook (metaphorically or literally). The Pensions Regulator (tPR) has imposed similar data quality requirements on pensions companies in the UK.
Even if there’s no specific obligation for your company to toe that line, it’s sound advice for any business.
Reaching a Shared Understanding of Data Quality
How is “Data Quality” defined and measured in your work? The words are so simple that everyone assumes they know what data quality means. But if everyone’s reaching their own definitions, how do you know they’re working in the same direction?
Clear goals and key performance indicators are vital, as is a shared terminology. It’s also essential that members of the board or data governance committee have the same understanding of data quality as the members of the DQ team.
When there are problems, everyone needs to grasp why. When proactive data quality controls are designed and implemented, the brief must match the requirements of the end users as well as the commissioners. When there’s a win, the board must understand what was won, why it matters and what degree of effort and dedication was involved.
Quality in All Things (Especially Data)
The greatest benefits and returns on investment are earned not only through an undertaking-wide understanding of data quality, but by the sheer power of an organisation-wide enthusiasm for optimising quality in all undertakings.
So what does data quality mean to you and your team? Is it an obsession, a labour or a seemingly unreachable mirage? Whatever your data quality situation, Match2Lists can help – try our lightning fast data matching, merging and deduping tools free today!
Get more return on your investment with Match2Lists When you choose a data quality tool, it's important to know what return you expect on your investment. How do you work out your ROI?
Read More Post a comment (0)Big data is the latest buzzword across the business world. How are you handling your company’s data volumes? What are the implications of big data for the way you work?
Big Data: The Facts
‘Big data’ is a term applied to datasets too large for a typical database application to handle. That’s a fairly flexible definition, so try this: if you’re wondering whether or not you have big data, then you probably haven’t. But many companies are managing a petabyte* or more of stored data already; even if yours isn’t among them, your future success may depend on your understanding of big data issues.
Big data is changing the way we do business. Cloud computing and other technological developments make it possible to store, compare, combine, deduplicate and analyse enormous volumes of data. Big data analytics supports and even automates fine-tuned decision making processes, adding value while conserving resources.
Managing Big Data
Compare the 40% projected growth in global data generated per year to the 5% growth in global IT spending*, and you can see that many companies will fail to keep up. Those who make the best use of this data torrent will increase their market share at the expense of their less forward-thinking peers.
Large companies and governments are therefore seeking out innovative ways to maximise the return on their big data. The more prudent smaller companies already have strategies in place to deal with increased data volumes, and to apply big data thinking in their business.
The Way Forward
Legacy systems and compatibility issues often stand in the way of seamless integration and sophisticated deep analysis of big data. There is now an exponentially increasing need for new technologies, methods and business models that make big data quality swift, smart and simple.
One very effective way to match, merge and deduplicate big datasets is with a Match2Lists Enterprise Account. Processing billions of potential matches in the cloud takes only seconds, while collaborating with colleagues or third parties speeds validation and ensures accurate results. No matter how big your data or where you keep it, if you can upload it to Match2Lists in text file format then your data matching, merging and deduping projects will be a breeze.
Add Value to Your Data
Whether you’re working with big data or one small list, Match2Lists has an account to suit your needs. If you’re not already using it, take a free trial now to see how it works for you!
*Source: McKinsey Global Institute
