Tag Archives: software

How to Resist Sexy Analytics Software

Who’s made the mistake of buying apps or sexy analytics software just based on appearance?

Go on, own up. I’m sure at one time or other, we have all succumbed to those impulse purchases.

It’s the same with book sales. Although it should make no difference to the reading experience, an attractive cover does increase sales.

But if you approach your IT spending based on attractiveness, you’re heading for trouble.

Now you may be thinking. Hold on, that’s what my IT department is there to protect against. That may be the case in your business, but as Gartner has predicted, by 2017 the majority of IT spending in companies is expected to be made by the CMO, not the CIO.

There are advantages to that change. Software will need to be more accessible for business users and able to be configured without IT help, and the purchasers are likely to be closer to understanding the real business requirements. But, as insight teams increase their budgets, there are also risks.

This post explores some of the pitfalls I’ve seen business decision makers make. Given our focus as a blog, I’ll be concentrating on the purchase of analytics software on the basis of appearance.

1. The lure of automation and de-skilling:

Ever since the rise of BI tools in the ’90s, vendors have looked for ways to differentiate their MI or analytics software from so many others on the market. Some concentrated on “drag and drop” front ends, some on the number of algorithms supported, some on their ease of connectivity to databases, and a number began to develop more and more automation. This led to a few products (I’ll avoid naming names) creating what were basically “black box” solutions that you were meant to trust to do all the statistics for you. They became a genre of “trust us, look the models work” solutions.

Such solutions can be very tempting for marketing or analytics leaders struggling to recruit or retain the analysts/data scientists they need. Automated model production seems like a real cost saving. But if you look more deeply, there are a number of problems. Firstly, auto-fitted models rarely last as long as ‘hand crafted’ versions, and tend to degrade faster as it is much harder not to have overfitted the data provided. Related to this, such an approach does not benefit from real understanding of the domain being modeled (which is also a pitfall of outsourced analysts). Robust models benefit from variable and algorithm selection that are both appropriate to the business problem and know the meaning of the data items, as well as any likely future changes. Lastly, automating almost always excludes meaningful “exploratory data analysis,” which is a huge missed opportunity as that stage more often than not adds to knowledge of data and provides insights itself. There is not yet a real alternative to the benefits of a trained statistical eye during the analytics and model building process.

2. The quick fix of local installation:

Unlike all the work involved in designing a data architecture and appropriate data warehouse/staging/connectivity solution, analytics software is too often portrayed as a simple matter of install and run. This can also be delusory. It is not just the front end that matters with analytics software. Yes, you need that to be easy to navigate and intuitive to work with (but that is becoming a hygiene factor these days). But there is more to consider round the back end. Even if the supplier emphasizes its ease of connectivity with a wide range of powerful database platforms. Even if you know the investment has gone into making sure your data warehouse is powerful enough to handle all those queries. None of that will protect you from lack of analytics grunts.

See Also: Analytics and Survival in the Data Age

The problem, all to often, is that business users are originally offered a surprisingly cheap solution that will just run locally on their PCs or Macs. Now, that is very convenient and mobile, if you simply want to crush low volumes of data from spreadsheets or data on your laptop. But the problem comes when you want to use larger data sources and have a whole analytics team trying to do so with just local installations of the same analytics software (probably paid for per install/user). Too many current generation cheaper analytics solutions will in that case be limited to the processing power of the PC or Mac. Business users are not warned of the need to consider client-server solutions, both for collaboration and also to have a performant analytics infrastructure (especially if you also want to score data for live systems). That can lead to wasted initial spending as a costly server and reconfiguration or even new software is needed in the end.

3. The drug of cloud-based solutions:

With any product, it’s a sound consumer maxim to beware of anything that looks too easy or too cheap. Surely, such alarm bells should have rung earlier in the ears of many a marketing director who has ended up being stung by a large final “cost of ownership” for a cloud-based CRM solution. Akin to the lure of fast-fix local installation, cloud-based analytics solutions can promise even better, no installation at all. Pending needing firewall changes to have access to the solution, it offers the business leader the ultimate way to avoid those pesky IT folk. No wonder licenses have sold.

But anyone familiar with the history of the market leaders in cloud-based solutions (and even the big boys who have jumped on the bandwagon in recent years), will know it’s not that easy. Like providing free or cheap drugs at first, to create an addict, cloud-based analytics solutions have a sting in the tail. Check out the licensing agreement and what you will need to scale. As use of your solution becomes more embedded in an organization, especially if it becomes the de facto way to access a cloud-based data solution, your users  thus license costs will gather momentum. Now, I’m not saying the cloud isn’t a viable solution for some businesses. It is. But beware of the stealth sales model that is implicit.

4. Oh, abstraction, where are you now I need you more than ever?

Back in the ’90s, the original business objects product created the idea of a “layer of abstraction” or what was called a “universe.” This was configurable by the business (but probably by an experienced power user or insight analyst who knew the data), but more often than not benefited from involvement of a DBA from IT. The product looked like a visual representation of a database scheme diagram and basically defined not just all the data items the analytics software could use, but also the allowed joins between tables, etc. Beginning to sound rather too techie? Yes, obviously software vendors thought so, too. Such a definition has gone the way of metadata, perceived as a “nice to have” that is in reality avoided by flashy-looking workarounds.

The most worrying recent cases I have seen of lacking this layer of abstraction are today’s most popular data visualization tools. These support a wide range of visualizations and appear to make it as easy as “drag and drop” to create any you want from the databases to which you point the software (using more mouse action). So far, so good. Regular readers will know I’m a data visualization evangelist. The problem is that without any defined (or controlled, to use that unpopular term) definition of data access and optimal joins, the analytics queries can run amok. I’ve seen too many business users end up in confusion and have very slow response times, basically because the software is abdicating this responsibility. Come on, vendors, in a day when Hadoop et al. are making the complexity of data access more complex, there is need for more protection, not less!

Well, I hope those observations have been useful. If they protect you from an impulse purchase without having a pre-planned analytics architecture, then my time was worthwhile.

If not, well, I’m old enough to enjoy a good grumble, anyway. Keep safe! 🙂

parties

In Third Parties We (Mis)trust?

Technology is transforming trust. Never before has there been a time when it’s been easier to start a distant geographical relationship. With a credible website and reasonable products or services, people are prepared to learn about companies half a world away and enter into commerce with them.

Society is changing radically when people find themselves trusting people with whom they’ve had no experience, e.g. on eBay or Facebook, more than with banks they’ve dealt with their whole lives.

Mutual distributed ledgers pose a threat to the trust relationship in financial services.

The History of Trust

Trust leverages a history of relationships to extend credit and benefit of the doubt to someone. Trust is about much more than money; it’s about human relationships, obligations and experiences and about anticipating what other people will do.

In risky environments, trust enables cooperation and permits voluntary participation in mutually beneficial transactions that are otherwise costly to enforce or cannot be enforced by third parties. By taking a risk on trust, we increase the amount of cooperation throughout society while simultaneously reducing the costs, unless we are wronged.

Trust is not a simple concept, nor is it necessarily an unmitigated good, but trust is the stock-in-trade of financial services. In reality, financial services trade on mistrust. If people trusted each other on transactions, many financial services might be redundant.

People use trusted third parties in many roles in finance, for settlement, as custodians, as payment providers, as poolers of risk. Trusted third parties perform three roles:

  • validate – confirming the existence of something to be traded and membership of the trading community;
  • safeguard – preventing duplicate transactions, i.e. someone selling the same thing twice or “double-spending”;
  • preserve – holding the history of transactions to help analysis and oversight, and in the event of disputes.

A ledger is a book, file or other record of financial transactions. People have used various technologies for ledgers over the centuries. The Sumerians used clay cuneiform tablets. Medieval folk split tally sticks. In the modern era, the implementation of choice for a ledger is a central database, found in all modern accounting systems. In many situations, each business keeps its own central database with all its own transactions in it, and these systems are reconciled, often manually and at great expense if something goes wrong.

But in cases where many parties interact and need to keep track of complex sets of transactions they have traditionally found that creating a centralized ledger is helpful. A centralized transaction ledger needs a trusted third party who makes the entries (validates), prevents double counting or double spending (safeguards) and holds the transaction histories (preserves). Over the ages, centralized ledgers are found in registries (land, shipping, tax), exchanges (stocks, bonds) or libraries (index and borrowing records), just to give a few examples.

The latest technological approach to all of this is the distributed ledger (aka blockchain aka distributed consensus ledger aka the mutual distributed ledger, or MDL, the term we’ll stick to here). To understand the concept, it helps to look back over the story of its development:

 1960/’70s: Databases

The current database paradigm began around 1970 with the invention of the relational model, and the widespread adoption of magnetic tape for record-keeping. Society runs on these tools to this day, even though some important things are hard to represent using them. Trusted third parties work well on databases, but correctly recording remote transactions can be problematic.

One approach to remote transactions is to connect machines and work out the lumps as you go. But when data leaves one database and crosses an organizational boundary, problems start. For Organization A, the contents of Database A are operational reality, true until proven otherwise. But for Organization B, the message from A is a statement of opinion. Orders sit as “maybe” until payment is made, and is cleared past the last possible chargeback: This tentative quality is always attached to data from the outside.

1980/’90s: Networks

Ubiquitous computer networking came of age two decades after the database revolution, starting with protocols like email and hitting its full flowering with the invention of the World Wide Web in the early 1990s. The network continues to get smarter, faster and cheaper, as well as more ubiquitous – and it is starting to show up in devices like our lightbulbs under names like the Internet of Things. While machines can now talk to each other, the systems that help us run our lives do not yet connect in joined-up ways.

Although in theory information could just flow from one database to another with your permission, in practice the technical costs of connecting databases are huge. Worse, we go back to paper and metaphors from the age of paper because we cannot get the connection software right. All too often, the computer is simply a way to fill out forms: a high-tech paper simulator. It is nearly impossible to get two large entities to share our information between them on our behalf.

Of course, there are attempts to clarify this mess – to introduce standards and code reusability to help streamline business interoperability. You can choose from EDI, XMI-EDI, JSON, SOAP, XML-RPC, JSON-RPC, WSDL and half a dozen more standards to “assist” your integration processes. The reason there are so many standards is because none of them finally solved the problem.

Take the problem of scaling collaboration. Say that two of us have paid the up-front costs of collaboration and have achieved seamless technical harmony, and now a third partner joins our union, then a fourth and a fifth … by five partners, we have 13 connections to debug, by 10 partners the number is 45. The cost of collaboration keeps going up for each new partner as they join our network, and the result is small pools of collaboration that just will not grow. This isn’t an abstract problem – this is banking, this is finance, medicine, electrical grids, food supplies and the government.

A common approach to this quadratic quandary is to put somebody in charge, a hub-and-spoke solution. We pick an organization – Visa would be typical – and all agree that we will connect to Visa using its standard interface. Each organization has to get just a single connector right. Visa takes 1% off the top, making sure that everything clears properly.

But while a third party may be trusted, it doesn’t mean it is trustworthy. There are a few problems with this approach, but they can be summarized as “natural monopolies.” Being a hub for others is a license to print money for anybody that achieves incumbent status. Visa gets 1% or more of a very sizeable fraction of the world’s transactions with this game; Swift likewise.

If you ever wonder what the economic upside of the MDL business might be, just have a think about how big that number is across all forms of trusted third parties.

2000/’10s: Mutual Distributed Ledgers

MDL technology securely stores transaction records in multiple locations with no central ownership. MDLs allow groups of people to validate, record and track transactions across a network of decentralized computer systems with varying degrees of control of the ledger. Everyone shares the ledger. The ledger itself is a distributed data structure held in part or in its entirety by each participating computer system. The computer systems follow a common protocol to add transactions. The protocol is distributed using peer-to-peer application architecture. MDLs are not technically new – concurrent and distributed databases have been a research area since at least the 1970s. Z/Yen built its first one in 1995.

Historically, distributed ledgers have suffered from two perceived disadvantages; insecurity and complexity. These two perceptions are changing rapidly because of the growing use of blockchain technology, the MDL of choice for cryptocurrencies. Cryptocurrencies need to:

  • validate – have a trust model for time-stamping transactions by members of the community;
  • safeguard – have a set of rules for sharing data of guaranteed accuracy;
  • preserve – have a common history of transactions.

If faith in the technology’s integrity continues to grow, then MDLs might substitute for two roles of a trusted third party, preventing duplicate transactions and providing a verifiable public record of all transactions. Trust moves from the third party to the technology. Emerging techniques, such as, smart contracts and decentralized autonomous organizations, might in future also permit MDLs to act as automated agents.

A cryptocurrency like bitcoin is an MDL with “mining on top.” The mining substitutes for trust: “proof of work” is simply proof that you have a warehouse of expensive computers working, and the proof is the output of their calculations! Cryptocurrency blockchains do not require a central authority or trusted third party to coordinate interactions, validate transactions or oversee behavior.

However, when the virtual currency is going to be exchanged for real-world assets, we come back to needing trusted third parties to trade ships or houses or automobiles for virtual currency. A big consequence may be that the first role of a trusted third party, validating an asset and identifying community members, becomes the most important. This is why MDLs may challenge the structure of financial services, even though financial services are here to stay.

Boring ledgers meet smart contracts

MDLs and blockchain architecture are essentially protocols that can work as well as hub-and-spoke for getting things done, but without the liability of a trusted third party in the center that might choose to exploit the natural monopoly. Even with smaller trusted third parties, MDLs have some magic properties, the same agreed data on all nodes, “distributed consensus,” rather than passing data around through messages.

In the future, smart contracts can store promises to pay and promises to deliver without having a middleman or exposing people to the risk of fraud. The same logic that secured “currency” in bitcoin can be used to secure little pieces of detached business logic. Smart contracts may automatically move funds in accordance with instructions given long ago, like a will or a futures contract. For pure digital assets there is no counterparty risk because the value to be transferred can be locked into the contract when it is created, and released automatically when the conditions and terms are met: If the contract is clear, then fraud is impossible, because the program actually has real control of the assets involved rather than requiring trustworthy middle-men like ATM machines or car rental agents. Of course, such structures challenge some of our current thinking on liquidity.

Long Finance has a Zen-style koan, “if you have trust I shall give you trust; if you have no trust I shall take it away.” Cryptocurrencies and MDLs are gaining more and more trust. Trust in contractual relationships mediated by machines sounds like science fiction, but the financial sector has profitably adapted to the ATM machine, Visa, Swift, Big Bang, HFT and many other innovations. New ledger technology will enable new kinds of businesses, as reducing the cost of trust and fixing problems allows new kinds of enterprises to be profitable. The speed of adoption of new technology sorts winners from losers.

Make no mistake: The core generation of value has not changed; banks are trusted third parties. The implication, though, is that much more will be spent on identity, such as Anti-Money-Laundering/Know-Your-Customer backed by indemnity, and asset validation, than transaction fees.

A U.S. political T-shirt about terrorists and religion inspires a closing thought: “It’s not that all cheats are trusted third parties; it’s that all trusted third parties are tempted to cheat.” MDLs move some of that trust into technology. And as costs and barriers to trusted third parties fall, expect demand and supply to increase.

How to Choose the Right CRM Package

Perhaps the most important thing an insurer can do to keep clients and brokers happy is to implement the right kind of customer relationship management system and process. CRM lets the insurer anticipate needs and communicate effectively. The most obvious benefits of a good CRM system are:

  • Accessible client information, with the ability to view it in multiple dimensions
  • An automated tool for reminders
  • The ability to document prospect and broker files

But those are just the baseline benefits. With a more comprehensive system, you get usability that exceeds these minimal expectations. It can bring an insurer to a whole new technological landscape that improves retention levels and increases efficiency.

Choosing the Right CRM

Before selecting CRM software, determine who’s considered a customer, because that will dictate the features the CRM software must have. Prospects and policyholders are certainly customers, but many insurers miss out when they neglect to recognize that brokers are customers, too. The CRM software chosen needs to serve them, as well.

For maximum efficiency, choose a CRM that has certain integration functions. It should connect with other sales technology systems that you and your brokers use often, because service is the key differentiator.

To take sales and service to the next level, the CRM system should allow for data to be entered once and then pushed out to other systems, including quoting and underwriting. Distribution channel and prospect information can then be populated into a sales and underwriting system. Not only is this a more streamlined way to conduct business, it also helps the process feel more personal and customized for each user. Every sales representative can have all her information immediately. It also provides for more effective self-service on the web.

One-time entry also makes selling much easier for brokers and sales offices of the insurance company, which will always have access to updated information. This, in turn, makes your products more accessible and appealing. An advanced CRM system will also make reporting and reviewing analytics easier, allowing insurers to identify issues more easily and respond to them more quickly.

Activity tracking is also an important feature. Having an accurate record of changes and updates is important in both relationship management and regulatory compliance. Regulators increasingly demand insurers be able to document compliance.

Finally, you want to make sure your CRM software has configuration options that will maximize its utility for your company and brokers. Every company is unique, and CRM software that forces you into its box isn’t useful. You should be able to tailor a CRM system to make it work more efficiently for you, not have to work around it.

CRM software isn’t just about tracking and storing information—it’s about creating a collaborative environment among product managers, brokers, carriers and clients. Let the data flow—in a well-organized, transparent way that treats every person as a distinct individual with her own needs and expectations.

The Incredible Impact From Superbosses

Please join me for “Path to Transformation,” an event I am putting on May 10 and 11 at the Plug and Play accelerator in Silicon Valley in conjunction with Insurance Thought Leadership. The event will not only explore technological breakthroughs but will explain how companies can test and absorb the technologies, in ways that then lead to startling (and highly profitable) innovation. My son and I have been teaching these events around the world, and I hope to see you in May. You can sign up here.

“I don’t care if you have to take drugs, you have to build it in six months,” said my boss, Khurshed Birdie, when I told him that he was on drugs if he thought my team could create a software development tool set in less than three years. This was in 1986 at Credit Suisse First Boston, one of New York City’s top investment banks. We were rebuilding the company’s trade processing systems to run on a client–server model of computing. This technology is common now, but then it was as futuristic as “Star Wars.”

My team worked day and night to build a technology that became the foundation of the company’s information systems. It gave Credit Suisse First Boston a competitive edge and led IBM to invest $20 million in a spinoff company that was formed to market the tools we had developed.

I was a lowly computer programmer, an analyst when Birdie hired me, a computer geek who didn’t own any three-piece suits, white two-ply cotton shirts or wing-tipped Oxford shoes — the uniform of investment bankers. Yet I was hired on the spot. I had some far-out ideas about how computer systems could be built but didn’t believe for a second that I could implement them. My boss did: He believed in me more than I did, and he bet a $100 million project on my vision.

He allowed me to expand my team from four to 54 people and shielded me from criticism by other teams who had to use my tools to build their systems — and who thought I was crazy. There were a lot of problems along the way, and Birdie allowed me to learn from my mistakes. And then he promoted me to vice president of information technology when I achieved success.

Birdie was what Sydney Finkelstein, a Dartmouth business professor, in his new book, Superbosses: How Exceptional Leaders Manage the Flow of Talent, calls a “superboss.”

As Finkelstein explains, superbosses take chances on unconventional talent. Oracle’s founder, Larry Ellison, hired candidates who had accomplished something genuinely difficult, rather than those with formal qualifications, because he believed they would rise to the technical challenges. Designer Ralph Lauren offered jobs to strangers whom he met while dining in New York City restaurants. Superbosses take raw talent and build self-confidence. They hire for intelligence, creativity and flexibility — and are not afraid of people who may be smarter than they are.

Under Finkelstein’s definition of superbosses, Birdie would be categorized as a “glorious bastard”: someone who cares only about winning. Deep down, he had a good heart —  but was ruthless in setting expectations and driving people to work extremely hard. I’ll never forget him telling me that “Christmas was an optional holiday.” These bosses realize that, to get the very best results, they need to drive people to perform beyond what seems reasonable and achievable.

Even though I achieved a lot, I hated working for Birdie, because I had to neglect my family for months on end. This isn’t something I would ever do to my employees. My next boss, Gene Bedell, was very different. He left his job as managing director of information technology to found Seer Technologies, the start-up that IBM had funded. Bedell convinced me to leave my high-paying investment-banking job to join him in a No. 2 role, as chief technology officer, at the low-paying, high-risk, start-up.

Bedell was what Finkelstein calls a “nurturer”: someone who coaches, inspires and mentors. These superbosses take pride in bringing others along and care deeply about the success of their protégés; they help people accomplish more than they’d ever thought they could.

Bedell managed by a method he called “outstanding success possibilities.” He challenged his executives to set ultra-ambitious goals and then find unconventional ways to achieve them. Instead of managing to what was achievable and possible, we shot for the impossible. And then did whatever it took to get there — without worrying about failure or looking back. It is amazing what you can achieve when you have a single-minded focus. We took Seer Technologies from zero to $120 million in annual revenue and an IPO in just five years — faster than any other software company of that era, including Microsoft and Oracle.

Superbosses create master–apprentice relationships. They customize their coaching to what each protégé needs and are constant fonts of practical wisdom. Bedell taught me how to sell. A year after the company was formed, he sent me to Tokyo to sell IBM-Japan on an $8.6 million deal to fund the creation of a Japanese version of our product. I didn’t think that a techie like me could do these things; he taught me that selling was an art that could be learned and perfected. I helped our salespeople close more than $200 million in software deals. And that is another skill that superbosses have, building what Finkelstein calls the “cohort effect”: teamwork and competition combined. Lorne Michaels, for example, who created “Saturday Night Live,” judged writers and performers by how much of their material actually went to air — but they had to do it with the support of their coworkers, the people they were competing with.

A common trait of superbosses is the ability to delegate work and build jobs on the strengths of their subordinates. They trust subordinates to do their jobs and are as supportive as can be. They remain intimately involved in the details of the businesses and build true friendships. Bedell often invited my family to his vacation home near the Outer Banks of North Carolina. He took me to Skip Barber Racing School to learn how to race a Formula Ford and built a gym in his basement so that his executive team could lift weights together.

You will find the alumni of our project at Credit Suisse First Boston and Seer Technologies in senior leadership roles now, at companies such as IBM, PayPal, American Express and every one of the top investment banks. Many started their own companies, as I later did. There are literally hundreds of people who built successful careers because of my two superbosses. When I became an academic later in life, I was fortunate to have two superboss deans at Duke’s Pratt School of Engineering, Kristina Johnson and Tom Katsouleas, who nurtured me. Superbosses aren’t just in corporations — they can be found everywhere.

Yes, I know that I got lucky in having good bosses; most are jerks who demotivate employees, slow their growth, backstab and take credit for others’ work. You are usually stuck with whomever you get. But there is nothing that stops you from being a superboss. As you begin to achieve success, start helping others and nurturing your colleagues and subordinates. Show the leadership qualities that you’d like your own boss to have. You will gain as much as the people you help — and build a better company.

This article first appeared at the Washington Post.

retirement

75% of People Not on Track for Retirement

A new study shows that three in four Canadians are not on track for retirement. With the recent economic turmoil, many working Canadians are struggling to make ends meet as it is. The same survey indicated that half the population is living paycheck to paycheck, and very few have any emergency savings built up. Living in the moment means that they’re not focused on retirement goals, and many expect to be working several more years as a result.

Although workplace pensions, the Guaranteed Income Supplement (GIS), Old Age Security (OAS) and the Canada Pension Plan (CPP) can provide funds, it’s often not enough. Moreover, the higher your income is now, the less likely you are to have your future needs met by these types of programs. If you’re among the 75% who are not on track to retire, here are the changes you need to make now:

Take a Hard Look at the Money Coming In

You’ll need to set a budget, but long before you get to it you must have a full accounting of how much money is coming into the household. Then, you’ll need to deduct between 20% and 30% of the gross for emergency expenses and retirement. Focus on building emergency savings that will cover you for three to six months first.

Eliminate Bad Debts

Carrying a balance for a mortgage or vehicle isn’t usually a problem, but more and more Canadians are maxing out credit cards and racking up other smaller debts. These things should also be knocked out of the way first.

Say Goodbye to Luxury Spending

While the older population is much better at assessing value and affordability, the younger generation is geared toward luxury items. Expensive cars, lavish clothing and trending technology add to debt. If you aren’t on track for retirement, and you’re carrying unnecessary debts, you should get yourself back on track and only purchase essential and value-oriented products.

Reevaluate Your Investment Choices

Unfortunately, many investment firms take a chunk of payments, and they fail to deliver in returns. Do a cost-benefit analysis and see if you need to consider moving your money to another firm or program. Diversification, both on a local and international level, is essential, as it provides a kind of insurance in case the economy falters. Think beyond stocks, as well. Bonds, commodities and real estate holdings can provide extra layers of security.

Use a Budgeting Program

There are numerous options available, but they all serve the same essential function. Using software or an app to track expenses takes the brainwork out of it and enables you to stick to your budget without having to work so hard.

Incrementally Increase Retirement Savings

As you pay off your debts and eliminate your mortgage, and your children become self-sufficient, you’ll obviously have more money to spend on yourself. Many people jump into doing the things they’ve been holding off on, like vacations and home remodels, but this becomes a slippery slope. As you find yourself free of expenses and debts, it’s imperative to increase your retirement savings, as well. During your last decade or two of work, your goal should be buildings toward setting aside 60% of your income for retirement. Some of the cash should go into savings, but a fair amount should be invested into dividend-paying stocks, which will add a steady trickle of supplemental cash as your non-working days progress.

Reevaluate Your Goals and Get Expert Advice

Even though most people can benefit from visiting with a financial planner, very few people do. You don’t have to be wealthy to benefit from one, either. A financial planner can help you figure out ways to minimize debts and how to save and may be able to help you get lower interest rates on the debts you already carry. If you choose not to visit a financial planner, you should still reevaluate your budget and strategy on a regular basis. This way, you can find ways to increase your savings if you aren’t setting aside enough, or enjoy more of your income now, provided you’re on track for retirement.

There was a time when a person could outright retire at a certain age, but it’s not like that any more. Today’s workers have to contribute more on their own to be able to maintain the same standard of living, and they have to work longer to be prepared. It’s still possible to retire at about the age your parents and grandparents did, but it requires more planning on your part.