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Business data has existed for ages. But mostly it just sat hopelessly trapped in handwritten ledgers, filing cabinets, and floppy disks, a precious resource untapped. Software from past decades helped only so much. Many such applications could work only with individual databases and were often costly and unwieldy to boot. Until very recently, such data could be used only by the giants.
Now, thanks to falling tech costs and new tools that display complex databases in ways even technophobes could love, smaller companies can unlock many more secrets from data. Your company’s databases can be cross-referenced with the expanding galaxy of information drawn not just from the likes of social networks, government databases, and usage patterns on mobile devices, but also from increasingly specialized information sources such as digitized transcripts of call-center interactions and sensors sending updates from various steps within a supply chain, and do so affordably.
Companies ranging from established giants such as IBM, SAS, and Microsoft to startups such as Tranzlogic and Kaggle offer affordable, cloud-based data-crunching services-which can help you get nondigitized data into data-crunchable form-and today virtually anyone can get his or her hands dirty in the great Big Data mud pile.
Businesses successfully mining Big Data are cross-referencing their internal information-pricing histories, customer traffic patterns-with multiple outside sources to increase revenue by understanding customers’ behavior better, reducing costs by eliminating inefficiencies and human bias, strengthening client bonds by anticipating clients’ needs, enriching service offerings with new knowledge, and giving employees new tools to perform their jobs better.
Should you still think this is for only ultradigitized and data-intensive businesses, consider that two of our success stories involve a zoo and a small-town Realtor. “The history of information systems and business is that the rich tend to get richer,” says Tom Davenport, a professor at Babson College and a pioneer in helping companies understand Big Data. “There are big companies that could afford it, and so prospered more than the smaller ones.” But now, he adds, “there’s nothing that says you can’t do this as a small business, too.”
Twiddy & company realtors, Duck, North Carolina
A family-owned company in a sleepy seaside village discovers the truth in the numbers.
The famed Blackbeard anchored his pirate ships along the 200-mile-long string of islands known as the Outer Banks, and the Wright brothers staged early flights near the windswept village of Kitty Hawk, but the sandy stretches arcing alongside the Carolina coast retain an appealingly laid-back feel. That makes the Outer Banks a perfect rustic getaway for stressed-out tourists.
Many of those travelers find vacation rentals through Twiddy & Company, a family-owned local enterprise that manages 998 homes on the islands, whether they are simple cottages or 24-bedroom beachfront mansions. Twiddy’s dual challenge is to satisfy guests while ensuring homeowners rent their properties as profitably as possible. But don’t let the Outer Banks’s sleepy feel fool you. Big Data has made a difference here.
Like many companies, Twiddy had amassed years of operational data inside spreadsheets-where it all was buried, really. “We kept running into the same obstacles,” says marketing director Ross Twiddy. “Unless we had a good way of looking at the data, how could we make good decisions?”
Twiddy settled on SAS’s business analytics tools, which distilled the company’s spreadsheets into a customizable format the company could share with homeowners and contractors. Before, Twiddy could tell homeowners the dates when their property was available to rent. Now, the company can offer pricing recommendations pinpointed down to the week, on the basis of market conditions, seasonal trends, and the size and location of a home, among other criteria.
To cite just one example: “We noticed that the week after the Fourth of July saw a dropoff in demand,” says Twiddy, and, armed with that knowledge, Twiddy started letting its homeowners tweak prices in January for that week. Since the company began making such recommendations, overall bookings have increased, and more homeowners recommend Twiddy as a property manager. The inventory Twiddy manages has increased more than 10 percent over the past three years.
Twiddy also cut costs 15 percent by comparing each contractor’s maintenance charges against the average of its 1,200 other vendors, identifying and eliminating invoice processing errors, and automating service schedules. Those savings alone freed up $50,000 in the company’s budget over the past two years. Not bad for an initial investment of $40,000. Twiddy hoped its Big Data spending would pay for itself in three years. The company met that goal in Year One.
“There’s truth in numbers, and this software helps you find it,” Twiddy says. “When we saw that happen for us, it was like tasting ice cream for the first time. It’s something you never forget.”
Point Defiance Zoo & Aquarium Tacoma, Washington
A zoo tames the Pacific Northwest’s notoriously variable weather.
Every business has gremlins, and for the Point Defiance Zoo & Aquarium, the weather had long been the peskiest. The Pacific Northwest’s wild weather fluctuations often make a mockery of any forecast, which means predicting zoo attendance-and therefore staffing-is difficult.
For years, Point Defiance used standard weather reports, with mixed results, and that wasn’t good enough. “Zoos live and breathe through their attendance,” says Donna Powell, the zoo’s business and administration service manager. “We needed to understand how that ebbs and flows, and when and why it shifts.”
Working with IBM and analytics firm BrightStar Partners, Point Defiance parsed its historical attendance records against years of detailed local climate data collected by the National Weather Service. This led to new insights that helped the zoo anticipate with surprising precision how many customers would show up on a given weekend. That in turn helped the zoo determine, down to the hour, how many employees should be staffing front gates, carousels, and other positions on peak days.
Some managers were skeptical when Powell put the data to the first big test on Memorial Day weekend in 2013. Typical for the Northwest, it rained on two of the three days, and the temperature never rose above 62. But Powell predicted attendance within 200 people-out of several thousand-and had adjusted staffing accordingly, a crucial flexibility for a zoo that employs 85 to 120 people, depending on how many customers are expected. The information “helped make staffing changes in every department,” Powell says. “Some people still thought it was a fluke, but we’ve done it over and over again.” For Memorial Day 2014, the prediction was even more accurate: The zoo’s projections were within 113 of actual attendance. And a closer look at the data enabled Point Defiance to boost membership 13 percent in the first quarter of 2014 by targeting discount campaigns to the Zip codes of its most frequent guests. “For a minimal investment of less than $4,000, we sold $60,000 worth of memberships,” Powell says.
Powell also focused on when people booked online and was surprised to learn many customers bought tickets in the late evening or early morning, when parents’ busy schedules eased and they could finally plan their family’s weekends. Knowing this helped Point Defiance pinpoint time-limited deals to increase ticket sales, and the zoo’s online ticket sales have risen 771 percent over the past two years. The spike in online sales didn’t diminish on-site ticket sales, either. Overall ticket purchases have set records for two consecutive years.
Now Powell is considering looking at the health data of the zoo’s animals to improve their care. “If you can do that for humans,” she says, “why can’t you do that for animals?”
A data-driven approach keeps an upstart car marketplace competitive.
The Internet offers huge opportunities for buyers and sellers of used cars, but one truism still holds: Nobody wants to buy a used car sight unseen. That fear of paying for a lemon online has helped keep used-car dealerships in business, despite consumers’ distaste for the hard sell long associated with such venues.
But Carvana, a Phoenix-based online car marketplace launched in 2013, saw in Big Data multiple opportunities to better consumers’ experience and its business. Among the company’s 50 staff members are some you would never find selling cars: It employs five data experts, including a former Wharton professor who oversees Carvana’s analytics.
Well before Carvana launched, the company contacted Kaggle, an online community of data scientists who compete and collaborate to solve Big Data challenges submitted by companies such as Merck and Facebook. Carvana wanted a better way to predict if cars purchased at auction would be lemons-“kicks,” in used-car parlance-and it anted up $10,000 in prizes for the best solutions.
A system emerged that kicked the kicks and let Carvana make shrewder bids at auctions. “We wanted to determine which cars up for auction didn’t meet our quality standards,” says co-founder Ernie Garcia. Thanks to the winners of Carvana’s Kaggle competition as well as data gleaned from other analytics about regional customer preferences and model availabilities, Carvana found it could sidestep lemons and buy better cars “for $500 below what similar cars would sell for.” That edge is helping Carvana meet its goal of offering its customers an average $1,500 discount from market prices.
Carvana also worked with its data-expert staff members to mine customer data and reduce the risk in its financing business. Although many car dealerships simply look up a buyer’s credit score, Carvana scans hundreds of variables across several databases-including full credit reports from multiple companies and searches of the LexisNexis legal and news databases-to predict the likelihood of defaults, better tailor interest rates for individual customers, and weed out suspect buyers.
The result, according to co-founder Ryan Keeton, is “meaningfully” fewer defaults and not one car stolen through fraud. (Citing competitive concerns, Carvana executives did not share specifics concerning default rates or total vehicle sales.) Online used-car marketplaces still haven’t replaced the banner-strewn used-car lots of yore, but Big Data is helping one such venture find its footing. As it also helps a constellation of other smaller businesses, be they old or new.
As Business Intelligence (BI) systems become more and more embedded into the critical, analytics-based DNA of corporate decision-making, it is more important than ever to keep the system up and running at the highest level of efficiency. This article discusses methods for providing real-time monitoring and historical trending information for your system performance.
According to Gartner, business Intelligence and analytics will remain a top focus for CIOs through 2017, with companies spending millions on traditional BI software, cloud BI services and now mobile apps and even social BI. However, as the type and number of BI solutions has grown, so too has the possibility of failure, of picking the wrong business intelligence software for your business problem or problems or of having end users not understand or properly use the solution.
To help you avoid a potential costly mistake, and get the most out of your BI software investment, CIO.com has put together a list of nine most common mistakes organizations make in regard to selecting and implementing a business intelligence software solution — and how you can avoide these mistakes.
BI Mistake No. 1: Not defining the business problem(s) you are trying to solve.“Companies [should] not rush into leveraging any BI tools unless they have a distinct business case,” says Scott Schlesinger, senior vice president and head of Business Information Management, North America, Capgemini, a provider of consulting, technology and outsourcing.
“One of the biggest [mistakes in] pursuing an analytics initiative is jumping in too soon without clearly defining what it is the company wants to accomplish,” Schlesinger says. “Companies will not be able to generate any real ROI if they don’t outline the business case first and determining why and where leveraging big data makes the most sense in their operations.”
“One of the biggest mistakes is buying for ‘general capability’ vs. solving a defined problem,” says Charles Caldwell, director of Solutions Engineering and principal solutions architect atLogi Analytics, a business intelligence company.
“Too many folks look for the one silver bullet tool that will solve all analytics problems they ever have without fully defining the immediate problem to solve. And that is why so many BI projects fail,” Caldwell says. Instead, “start with the business problem to be solved, understand the specific capabilities required to solve those problems and then purchase the BI tool(s) that meet those specific needs.”
BI Mistake No. 2: Not getting buy-in from end users (before you choose your BI solution) “IT has a tendency to purchase BI tools in a vacuum, without first getting buy-in from the people ultimately expected to use them,” says Joanna Schloss, business intelligence and analytics evangelist, Dell Software. But “assuming employees will use newly purchased BI technologies simply because the organization is standardizing on them is a mistake,” she continues.
“Even the best BI tools are ineffective if they’re not utilized, and no amount of training or standardizing will convince people to use technology they don’t feel benefits them personally,” she explains. The solution: “Instead of telling employees they have to use something, help them clearly understand why they’ll want to use it. Clearly articulate the value proposition and adoption will follow.”
“Companies underestimate the difficulty in changing corporate cultures to accept and use the output of BI systems,” adds Ray Major, chief strategist, Halo Business Intelligence. “A successful implementation, regardless of which technology you choose, mandates that a company have both executive buy-in and end-user buy in. End-user buy-in requires a concerted and focused internal marketing and educational effort to highlight the benefits of the new BI system,” he says. “To ensure successful adoption, companies can influence end-user behavior by tying individual employee goals to metrics driven results.”
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BI Mistake No. 3: Not factoring in security or legal requirements. “Don’t make the mistake of forgetting about data governance [when selecting your business intelligence solution],” says Steve Farr, senior manager, Product Marketing,TIBCO Software, a provider of BI software. “Giving all the data to all the people and just letting them explore is unwise.” To protect the company and your customers, “make sure your new BI system works in accordance with your company’s data security policies and legal obligations.”
BI Mistake No. 4: Being dazzled by features and forgetting about legacy systems and integration. “Most companies evaluate BI [solutions] in terms of the features available in the tool, such as reporting and querying, dashboards, exploration and discovery, OLAP and analyses, data visualization, predictive analytics and performance management KPIs (e.g., balanced scorecards),” says Kiriti Mukherjee, director of Information Management, Collaborative Consulting.
Yet they forget one very important feature: integration. “Integration with office applications (many tools integrate with Excel, but far fewer do so with Outlook), embedding BI objects in other applications or enterprise portals and integration with thin and thick clients, including with custom mobile devices,” Mukherjee says.
They also forget about “integration capability with enterprise applications (such as ERP), cloud applications (Salesforce.com) or contextual services (MDM, DQ or external services such as D&B),” Mukherjee says. The point: While features are important, it is also important to make sure the BI solution you choose integrates well with your other business systems.
BI Mistake No. 5: Not choosing a solution that can scale and adapt. “One of the biggest mistakes you can make is choosing a solution that’s not agile,” says Francois Ajenstat, director, Product Management, Tableau Software, a data visualization company. “At fast-moving, cutting-edge companies, self-service analytics are becoming the norm,” he explains. “The monolithic infrastructure stack is crumbling in favor of solutions that can work with new data sources, and flexibility and usability from dashboards are key.”
You also want to make sure the business intelligence solution you choose can grow, or adapt, to your business needs, so you do not have to look for a new BI solution right away.
BI Mistake No. 6: Not factoring in the mobile workforce.“Many companies forget to consider mobility [when selecting a BI solution],” says Farr. However, “how we consume BI is as important as what we consume. In some cases a simple KPI displayed on a smartphone is as useful as all the paper-based reports in the world.”
BI Mistake No. 7: Rushing implementation. “A successful deployment is not always a rapid deployment, and a rapid deployment is not always a successful one,” says Daniel J. Ronesi, director, Business of Law Services, Aderant, a provider of legal software. When deploying a BI solution, patience is a virtue. “It is imperative that the implementation is not overly rushed so that sufficient time is set aside for training to ensure users are given the time to develop or acquire the skill sets needed to use the BI software effectively.”
“Deploy your business intelligence software incrementally,” advises Southard Jones, vice president of Product Strategy, Birst, a provider of cloud-based BI solutions. “Rather than expecting to solve every business problem all at once, prioritize specific outcomes you want to achieve. When you have answered the first business problem, add on incrementally and be flexible in your approach,” Jones says.
“Consider what answers will validate a recently introduced strategy or will have the biggest impact on your business operations,”Jones says. “Then choose one as a starting point. While business intelligence can eventually answer all of your questions, don’t expect all of the answers all at once.”
BI Mistake No. 8: Insufficient training (and miscalculating the costs of training).“Many organizations exhaust their BI budgets on software licenses and a few short weeks of training for their users,” says Steve Litwin, president, Litcom, a provider of IT consulting. However, “today’s BI systems are complex structures that require far more training in order for users to be able to acquire genuine value from them. [And] ongoing training is [necessary] so that users become familiar and comfortable with the system,” he says. “For example, organizations can host weekly lunch sessions where a different aspect of the BI system is discussed.” In addition, organizations should make sure the software they install comes with online training videos that help users “become better acquainted with the new BI system.”
BI Mistake No. 9: Not leveraging intelligence (collected data) and reporting. “Some companies collect valuable information from their BI software, but then don’t share it, analyze it or act on it,” and that’s a big mistake, says Joe Gerard, vice president, marketing and sales, i-Sight, a provider of case management software. “It’s important to think outside the box when assessing what to do with BI. By leveraging the information gathered and applying it to their own business models, companies can avert risk and make informed decisions to drive their business forward.”
BI software “can be used to report on many different data points, identify risks and opportunities and forecast trends,” Gerard says. Yet “many companies become complacent with a pre-defined set of reports and don’t take into account the changing business environment,” he says. Instead, companies should “maximize the reporting capabilities of their [BI] software, [so that they are] better able to predict and head off problems and identify risks.”Writen by Jennifer Lonoff Schiff Jennifer Lonoff Schiff is a business and technology writer and a contributor to CIO.com. She also runs Schiff & Schiff Communications, a marketing firm focused on helping organizations better interact with their customers, employees and partners. Original Link : http://www.cio.com/article/2464167/business-intelligence/9-common-bi-software-mistakes-and-how-to-avoid-them.html
Some SMB managers and entrepreneurs still think that business intelligence solutions are sophisticated and expensive tools needed only by large enterprises managing enormous amounts of data. But is it true? These are six reasons why I think it’s not.
Business Intelligence, despite its sophisticated aura, is a very straightforward practice. It is all about collecting relevant data about your company performance and taking actionable conclusions from it so you can make informed decisions on how to improve different aspects of the business.
I am sure you are already gathering data, analyzing it, looking for trends and valuable insights but probably using multiple spreadsheets, going over lengthy manual and inefficient processes to make calculations, identify patterns, come to conclusions and generate reports to share with your colleagues.
A business intelligence solution could help you to automate and simplify all these processes, offering you insightful dashboards, visualization tools and rich reports to easily attain better insights and make more informed decisions. So why are you not using one of them?
If you still think that BI solutions are expensive, complicated and difficult to implement, you are wrong. I contacted three developers of popular business intelligence solutions for SMBs – BIME, SiSense and Zoho Reports – to get their feedback on this topic. They confirmed my own analysis and gave me insightful reasons to share with you.
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A few years ago, because of their complexity and specific requirements, business intelligence solutions could be installed and administered only by IT departments that only large companies could afford to have. But this has changed dramatically, with a positive impact on the price and ROI of BI software.
Now most business intelligence applications are cloud-based and ready to use, without any specific hardware or implementation requirements. Their interfaces, dashboards and reporting capabilities have improved radically, making them very user-friendly and easily manageable for any employee in a company.
Business intelligence is not only about a company’s own data. With the rise of open data projects, SMBs can have access to data about economic and social trends that, when correlated with a company’s own data, can reveal previously unseen market trends. Thus, SMBs can level the playing field with big companies by identifying the opportunities in various areas faster.
Not only has business intelligence software penetrated SMBs with more affordable, scalable solutions, but using BI in small to medium size businesses is proving to be more rewarding for companies. This is mostly because SMBs have the ability to be agile and react quicker to freshly uncovered insights and make true data-driven decisions, which can be an advantage over slow-moving and larger organizations.
Every company today collects data that customers or prospects leave behind from web, email, social media, and mobile, making it easy for even SMBs to accumulate more data than they know what to do with. Just the data from marketing and sales departments alone can be overwhelming and easily exceed Excel’s capacity. In addition, almost every business today stores all their transactional data in a CRM solution and marketing data in Google AdWords and Analytics, which adds an additional growing data source to the equation.
Large data isn’t the only reason companies need BI software, it is also about joining multiple data sets into a single version of truth to build dashboards with information that will show you a complete picture of your business. Data in every company, regardless of size and number of customers, is growing exponentially and the information this data holds is invaluable to every business.
Still in doubt of the advantages of implementing a business intelligence solution for your SMB? We will follow up this post with other helpful articles about the importance of BI for small and medium companies.
Ready to choose and try a business intelligence solution? You can check BIME, SiSense and Zoho Reports, as these are apps designed for SMBs needs. You can also compare these three popular solutions or explore other BI solutions.
Read more at http://www.business2community.com/business-intelligence/6-reasons-smbs-use-business-intelligence-solutions-0847185#Uv3L4ewqjbFo84Dp.99