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Let\u2019s look at a fraud reduction group in a benefits provider as an example. How about Acme Benefits. We might expect the following answers to the questions:<\/p>\n
The data is a mess. The systems were set up to pay claims and report to customers. Analytics was never considered.<\/p>\n<\/li>\n
Developing analytics to reduce fraud would be a huge competitive advantage if it were successful. Accurate algorithms shouldn\u2019t be shared through crowd-sourcing.<\/p>\n<\/li>\n
Fraud analytics is one of the most valuable applications of machine learning. Tens or even hundreds of millions of dollars are on the table and the cost is a pittance compared to this.<\/p>\n<\/li>\n
Acme is skeptical of analytics and wants to see some actual results in the first quarter.<\/p>\n<\/li>\n<\/ol>\n
Looking at Acme\u2019s results, they\u2019re going to need to look outside for help. The time to clean up their data and to demonstrate value precludes them building from scratch. But outsourcing is problematic as well. There will be ongoing work for years to come and paying that all to a consultancy doesn\u2019t make a lot of sense. \u00a0<\/p>\n
Thankfully, the choice isn\u2019t simply to rent or to buy. We\u2019ll look at four in this article: insourcing, outsourcing, co-sourcing and crowdsourcing. \u00a0<\/p>\n
Note that there isn\u2019t a category for automating. There is no software that will magically give you analytics capability. If a software vendor is telling you this, then show them the door.<\/p>\n
Many organizations build analytics capability the traditional way. They have lots of data with obvious insights hidden within. They can justify the investment on value, but there\u2019s not huge time pressure to get it done. So they hire themselves a team. Let\u2019s call this insourcing<\/strong>.<\/p>\n Ideally, they start by recruiting a data science cowboy who can do a little bit of everything. The cowboy then hires his own team of data engineers, information designers, and analysts. \u00a0<\/p>\n In the most successful organizations, the analytics group has the five roles<\/a>, their leader has a seat at the executive table, and the team has a mandate to cross functions. They can respond to requests from other departments or they can pursue their own ideas. They have access to the entire organization\u2019s data and they have influence at the highest levels of decision making.<\/p>\n This model is often called an Analytics Center of Excellence<\/strong>\u00a0and it operates as an internal consulting group advising the organization over the long term. Team members develop company-specific domain expertise and gain the trust of the organization. Airlines, cities, and sports teams have embraced this concept with stunning<\/a>\u00a0results<\/a>. In the best case, they help change the culture of the entire organization to be evidence-based.<\/p>\n So what\u2019s the downside? First, there\u2019s no guarantee that your team will be competent. Anyone can put \u201cdata scientist\u201d on a resume and there is a huge difference between academic competence and real-world competence. Most organizations are incapable of differentiating between a skilled analyst and a charlatan. \u00a0<\/p>\n Furthermore, it\u2019s going to take time for this group to start showing results. They need to learn about your business, clean up your data, and build some credibility.<\/p>\n Finally, once your group is up and running, it\u2019s like any other department or division. It doesn\u2019t have to compete to stay in business, so it may get complacent and bureaucratic.<\/p>\n The next obvious option is outsourcing. The traditional consultancies all have analytics capabilities — or at least claim to. (Competent data scientists are difficult to keep around and often don\u2019t play well in the traditional consulting model). There are also a number of new firms that specialize in analytics consulting. \u00a0<\/p>\n Some firms are functional specialists with specific expertise in marketing analytics or HR analytics. Some are built around a specific industry like financial services or pipelines. Still others are built around a specific capability like machine learning or data visualization. As with any vendor, due diligence is a necessity.<\/p>\n You\u2019ll want to outsource when you need results quickly but intermittently — perhaps a four-month project every year. A competent consulting group will set a high bar for the analytics, but will also understand education, adoption, and the difficulty in transitioning to an evidence-based culture. The vendor will propose a team, scope out the project, and then deliver to a specific set of outcomes.<\/p>\n For these types of projects to be successful, you want experienced consultants with deep domain or industry expertise. You can\u2019t afford to pay for them to learn on the fly. When it works well, you get high quality results quickly and are happy to pay the fees because the value is so much more.<\/p>\n The downside of outsourcing is the same as with any consulting agreement: misaligned incentives, scope creep, inexperienced team members, and high costs. You have to trust that they\u2019ve considered the details and that they\u2019re not going to sell their newfound skills to one of your competitors. It\u2019s worthwhile to consider incremental or iterative approaches and to explore alternative contractual arrangements (fixed price, shared benefits, bonuses, etc.).<\/p>\n Finally, your organization likely won\u2019t know how to fish once the consultants leave.<\/p>\n Co-sourcing is a hybrid approach that tries to have its cake and eat it too. It ends up being an insource model, but it gets there more quickly by leveraging the expertise of a consulting partner.\u00a0<\/p>\n The idea is to start out with an outsourced consulting team, but in a longer term relationship. They don\u2019t simply deliver a single project result. Instead, they start by setting a strategy, prioritizing a set of projects, and beginning to educate the organization. As the engagement progresses, the consulting team helps the organization hire replacements for itself. Over time, the team size remains roughly the same, but fewer and fewer of them are consultants until eventually, it\u2019s an internal team.<\/p>\n Most consultancies have flirted with this approach, but in many cases it\u2019s misaligned with their strategy. If your business model is to sell fish, then you shoot yourself in the foot when you teach others how to do it. Sometimes an engagement with a university can work: professors and grad students act like the consultants and over time, some of them form your core team. But be careful. Academic goals are not aligned with your organization\u2019s.<\/p>\n The benefit of co-sourcing is its speed. You get quick results to analytical problems and you get a ready made team at the end of the engagement. The consultants take on much of the difficult change management and infrastructure work while still delivering results. The whole process may take a year or more to complete, but in the end, the client has a team, data infrastructure, some data products, and an engaged organization that has proven the value of analytics.<\/p>\n The downside of this approach is that it\u2019s expensive. You\u2019re paying someone else to do analytical work along with HR (recruiting and training), change management, and data infrastructure upgrades.<\/p>\n Let\u2019s take a quick detour to Acme Benefits here.<\/em><\/strong>\u00a0It appears that co-sourcing will best meet their needs. It will bring them quick results in a particularly complex environment. But it will also build the ongoing capability that they require. As an added bonus, Acme operates in an environment where the potential gains from analytics are extremely high. A co-source partner would likely find enough value in the first few months to pay for the entire group for years to come.<\/p>\n Crowdsourcing is a completely different animal. It\u2019s not about building general analytical capacity, but instead, accessing deep capability for specific problems. Typically, crowdsourcing is organized as a contest with a \u201cprize\u201d for whoever gets the best result. Netflix famously offered a million dollar prize<\/a>\u00a0to whoever could improve their movie rating algorithm by 10%. Over 40,000 teams entered and spent the next three years testing different approaches. \u00a0<\/p>\n<\/div>\n