A question about IT change management: does the DNA of the company fit your IT vendor?


Company_Vendor_DNAs

When delivering my final dissertation of the MBA program (here the link of a short presentation), along the research I’ve encountered the topic of IT Change Management.

As a matter of fact, whenever a company decides to implement IT innovations most likely new collaborations or partnerships with IT Vendors, consultants or third parties are needed. Usually, within a selection process, IT suppliers are evaluated accordingly to theirs know how and proven expertise. However, what about other aspects such as the agility to change, the ability to innovate and corporates’ cultures? Is there a potential fit or a misfit between the company and the selected IT vendor?

The company’s DNA

In order to avoid failures, it’s fundamental to set a pace for IT innovations that is affordable to the company according to its DNA. According to R. Ray Wang (@rwang0) there are two kind of attitudes when defining a DNA of a company: proactive vs. reactive and incremental vs transformational attitudes.

Cautious Adopters: proactive & incremental (about 30%). Such companies are looking for new technologies without waiting what other competitors do. However, they are willing to implement only the technologies that might play a key role in the future as well as they are not keen to consider the opportunity to change their business model even if the new technology enable a breakthrough.

Market Leaders proactive & transformational (about 5%). A market leader has the ability to sustain high paces of IT innovations as well as an organizational flexibility to change also its business model.

Laggards: reactive & incremental (30%). Such a company avoid any kind of risk of a self-disruptive innovation and integrates new technology only when other competitor succeed. In any case, without transforming its business model.

Fast Followers: reactive & transformational (15%). This of kind of DNA is able to mitigate the risk of adopting new technology by relying on the ability to change quickly the business model and the organization as a way to survive against disruptive innovation threats.

(More: “The Building Blocks of Successful Corporate IT“, HBR Blog)

IT Vendor’s DNA

What about the DNA of an IT Vendor? Gartner is well-known for providing a “magic” quadrant for everything and also for evaluating an IT vendor: completeness of vision and ability to change are the two main attitudes to consider.

Leaders: high completeness of vision & high ability to execute. As IT vendors, they are able not only to provide innovative services that works today but also to influence the market that theirs innovations are the best for the future. For these reason, such IT vendors might fit best a company with a leadership that wants to invest in new infrastructures\technology early and avoiding any risk due to technology (obsolescence, maintenance, etc.). However, also a cautious adopter (DNA) company that wants to develop a leader DNA should prefer IT leaders by relying on their ability to execute and play a key role as an influencer within a change management process.

Niche Players: low completeness of vision & low ability to execute. Is the case of IT vendors specialized in few functionalities and with low ability to execute due, for example, to a lack of resources (financial, operating) and power (network). However, such IT vendors might be useful for companies that need small technology changes without stringent delivery deadlines. For these reason IT niche players might be extremely useful for Laggard (DNA) companies.

Visionaries: high completeness of vision & low ability to execute. Is the kind of IT vendor that fit best a Cautious Adopter company’s DNA. Anyhow,  a Fast Follower (DNA) company that wants to innovate proactively rather than reactively, might get some useful insights from Visionaries third parties.

Challengers: low completeness of vision & high ability to execute. Is what Fast Follower companies usually need. However, a Cautious Adopter company that wants to improve its change management process should look for Challengers as IT vendors.

(More: “How Gartner Evaluates Vendors and Markets in Magic Quadrants and MarketScopes“, Gartner)

So, which IT Vendor to chose? Thinking about possible threats due to cultural and organizational divergences between the company and th IT vendor DNAs will ensure the implementation of the strategy as well as it will avoid market\operational risks and a waste of resources: why to invest on IT Vendor Leaders? Does the company really need it?

As a moral of this story, selecting the IT Vendors that fit best the company DNA is not so different as chosing relationships and friends in our every day life. Trusted and better relationships are guaranteed only by knowing ourselves as well as the others.

Feelink – Feel & Think approach for doing life!

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Firm Infrastructure Vs Catalyst to Change Business Models (part 2): New IT Innovation Development (NITID)


Value chain

Acknowledged the role of IT and technology as a catalyst to change business model (see the previous post “Firm Infrastructure Vs Catalyst to Change Business Models:  the Double Side of IT“), how to implement new IT innovations in practice?

Each innovation implies a changing process and each changing process implies commitment and investments…so, which IT innovations to choose in order to avoid a waste of resources? Are the IT innovations under development in line with the customer needs (actual or potential)? Which are the key competences needed for developing a new IT innovation?

Whatever the industry of the company is about, since the focus is customers’ needs, why not thinking how to adapt the NPD (New Product Development) process to IT innovation? The NPD is a structured creativity process focused on introducing new kind of products\services that effectively produces innovations.

In particular, the NPD process combines lateral (generations of ideas) and vertical thinking (selections of ideas) together and it is divided into four stages: ideas brainstorming, ideas classifications, ideas evaluation and ideas selection.

NPD stage 1: ideas brainstorming (lateral thinking)

In this stage all the team members engaged simply write down new ideas without any kind of filter or criticism (lateral thinking). Taking as an example the case of TripAdvisor (see “Tripadvisor: a case study to think why bigdata variety matters“) where a validation process of the reviews is in place by recording on a database the receipts of the end-user at the restaurant: which might be the new innovations available by using the data of the receipts?

NPD stage 2: ideas classification (vertical thinking) – KJ Method

Now, since usually a brainstorming session generate chaos, how to figure out which IT innovations to implement?

Like navigating in the middle of a storm, just stay focus on the ongoing  issues, do not think to a final solution and keep clam. So a first step is to organized ideas in a structure way in order to figure out a big picture. An approach for classifying the brainstormed ideas is the KJ method, where all the initiatives are split into groups by using a criteria. For example, criteria for classifying IT innovations could be: which are the departments\functions involved by the IT innovation? Or\And, which are the stakeholders (customer, suppliers, third parties,…) involved?

Then, for each group of ideas, assign a tag that identifies it. For example, suppliers IT innovation, marketing & sales IT innovations, and so on.

NPD stage 3: ideas evaluation (vertical thinking) – QFD Matrix

Once ideas are grouped and tagged, the next step is to identify the key performances that are needed in order to implement new IT initiatives. Typically, regarding IT stuff, they are about DataBase (storage, number of transactions,…), architectures, maintenance costs, usability, interoperability,…

By putting IT initiatives into rows and the key IT characteristics into columns, the finial result is a matrix called QFD (Quality Function Development). Briefly, the QFD matrix connects the IT initiative with the needed performances. For these reasons, the QFD matrix applied to IT innovations might be useful also for procurement: which are the key competences? Make or Buy? If buy, which IT vendor to choose?

NPD stage 4: ideas selection (vertical thinking) – Pugh Matrix

Just for a recap. We have organized the ideas, we have identified the key characteristics for each IT initiative… and so? Which IT initiative to implement? The answer is provided by the so called Pugh Matrix where, for the development of a new product or service, evaluates ideas and solutions according to a gap analysis. In particular, how much the new idea will be valuable for the customer? Will the new idea provide a competitive advantage against competitors?

A similar evaluation should be adopted also for IT innovation. Why? Just think about the risks correlated when IT becomes so complex to be maintained and thus a nightmare for customers, employees and suppliers as well.

Too much enthusiasm on IT initiatives has a side effect to much IT complexity. How to innovate without adding superfluous IT complexity? What about using NITID (New IT Initiative Development), a revised NPD method widely use for product innovations?

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Semantic search algorithm, behaviorism and fairy-tale Snowwhite with the seven dwarfs. Would SEO behave like Grumpy?


How does semantic search work? Which are the implications regarding SEO tactics and users/customers’ behaviors?

Google search is not unlike the “Mirror, mirror on the wall, who’s the fairest of them all?” where the question asked, reveals (in the fairy tale) the Evil Queen’s narcissistic obsession

, what a great metaphor to explain how semantic search works! (see Google Search and the Racial Bias).

I will take the assist from David Amerland to help me to better understand how the SEO world (something still unknown from me) as well as remembering childhood times with the fair tale “Snow White and the seven dwarfs“.

So, let’s have a look at the characters of the famous fairy-tale:

The mirror is the result of the search engine. According to what I’ve understood about semantic search, the mirror reflects back a result that is contextualize accordingly to the user and his/her relationships among the social networks as well as thorough the analysis of past behaviours.

Snow White is the most beautiful creature in the WEB forest. She publishes smart content as well as she establishes such trusted relationships in the social medias so that the mirror (the semantic search engine) reflects back a beautiful princess… accordingly to the algorithm I would say.

The evil queen is the bad guy, attempting to be viewed as the most beautiful in the WEB forest while it is not. The evil queen struggles and suffers a lot for that, since the mirror suggest always Snow White as the best result… the life in the digital jungle is not so easy for the evil queen!

The poisoned apple represents a trick, a negative SEO attack where the objective either is to game the search engine (the mirror) or to compromise the reputation of Snow White. Fake reviews, negative or positive SEO tactics, are just an example of how an apple could be poisoned in order to kill digitally a competitor and game the search engine algorithm (see the case of Tripdavisor).

The seven dwarfs are data scientists and SEO experts that are mining the WEB forest in order to get some valuable and reliable information from the WEB. Usually they are well-intentioned and thus willing to protect the beauty of Snow White from negative SEO (the poisoned apple and the evil princess).

The charming Prince represents all the users, companies and individuals, that go deeper and deeper into the WEB forest in order to discover the truth. Mirror’s result apart: Who is really the fairest in the WEB forest?Encountering few smart dwarf might be useful for the charming Prince, both in the forest to discover the beauty of Snow White and in the WEB to find out great contents and reputations accordingly to personal impressions rather than only relying on algorithms.

…so, which is the moral of the fairy-tail “Snow White and the seven dwarfs” applied to the modern semantic search and SEO?

An interesting point has been pointed out by D. Amerland in his article “How semantic search is changing end-user behaviour“. In particular:

The fact remains that the web is changing, search has changed and the way we operate as individuals, as well as marketers, has changed with it.

Since the semantic search is so powerful to influence the behaviour of the end-user (individuals, companies,…), the point is: what kind of algorithm there is behind the mirror on the wall? Which are the criteria behind the result that identify the fairest princess in the WEB?

More interesting doubt: what happen if the criteria behind the search algorithm (the mirror) change so that the fairest in the WEB would be Grumpy, one of the seven dwarfs? Would all the end-user and SEO really want to become and behave like Grumpy?

seo_mirror_on_the_wall

Saving IT Expenditures and sourcing Business Intelligence and Analytics tools: which are the KPIs for BigData?


The IT expenditures are forecasted to growth due to the business speculation behind the buzz word BigData. Therefore, in order to avoid biased insights, How to measure the quality of the information extracted? How is it possible to get more value from BigData and saving useless expenditures?  Are the insights provided by business intelligence, analytics, and prediction tools really reliable?

A meaningful example about the speculation behind BigData, is the case of sentimenters that analyze tweets and posts: are they a BigData bubble? (see also My Issue with BigData Sentiment Bubble: Sorry, Which Is the Variance of the Noise?)

Bigdata_process

As a matter of fact, insights and predictions are final results of a transformation process (see figure) where the data in input is elaborated by an algorithm. A fantastic and exhaustive explanation about the process behind any business intelligence tool is the pyramid of data science where five stages are identified: 1) data gathering/selection, 2) data cleaning/integration/storage, 3) feature extraction, 4) knowledge extraction and 5) visualization (see The Pyramid of Data Science).

Anyhow, as for the production of products such as cars, clothes and a good meal in a restaurant, high quality results are ensured by the quality of the raw materials and the quality of the transformation process as well.

Thus, if the raw material for an analytic is the Data, how to assess the quality of the supply, the data? ACID compliance and pessimistic looking already define best practices in order to guarantee the quality of data in terms of data management and thus reduce maintenance cost and improve efficiency.

However, from a procurement point of view, how evaluate the quality of the supply within a procurement process for sourcing data? Similarly, how to evaluate the quality of the process that transform the supply (data) into insights and valuable information?

Like for the production of products a well-defined procurement process is ensured through well written specifications documents where requirements and key parameters/characteristics are clearly stated. Superfluous to say that a well-defined procurement process will ensure the quality of the supply, and the quality of the supply will ensure the quality of the final product/service. In this case, it will ensure the quality of the insights.

Undoubtedly, the huge amount of data available nowadays and new technologic improvements are generating more business opportunities than in the past both to improve process efficiency and to define new business models (see Firm Infrastructure Vs Catalyst to Change Business Models: the Double Side of IT (Change Management).

Thus, clearly define which KPIs to look for and negotiating the aspects that really matters will ensure best IT analytics services in terms of opportunities to exploit, thanks to the insights provided, as well as saving costs.

As an example, if are not yet available parameters for evaluating the quality of data and of the analytics as well, I would go to a restaurant where I know the quality of the suppliers instead of looking to reviews and advertisements based on questionable data suppliers: fake TripAdvisor’s users (see Tripadvisor: a case study to think why bigdata variety matters).

Beeing aware that ignoring bigdata opportunities means also ignoring a better restaurants, with delicious meal and low prices, Companies that define best a procurement process for data sourcing will enjoy the best meal in a bigdata restaurant.

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Tripadvisor: a case study to think why bigdata variety matters


tripaadvisor

The recent scandals about fake reviews has put the reliability of TripAdvisor under discussion (see The Guardian).

Such a bad quality of service is not useful for consumers, entrpeneurs as well as in the long run for the reputation of TripAdvisor. So, where is the problem?

Clearly it’s a question of reliability of the sources of information and specifically for TripAdvisor is a question of assessing the reliability of the user that post a new review. Nice and easy…like discovering the hot water. However, thinking also at the practice of the so-called Negative SEO, that is not only an issue of web sites like TripAdvisor but also for all the companies that have to promote theirs brands in the social networks (who think doesn’t need it, raise up the hand).

In order to fix the issue, Tripadvisor developed the service Report Blackmail that tracks and eventually bans the users that are using Negative SEO tactics. For example, 100 user managed by a restaurateur that are reporting cases of colitis and runs in the reviews of the competitor near the corner. Such a solution try to catch fake users when they’ve already done the “attack” as well as, if not properly working, it might ban by mistake honest users. It sound reactive rather proactive, isn’t it?

So, are there other approaches that can fix the problem of malicious reviews proactively? An idea could be use new IT bigdata technologies and re-think the business model. How? (see also MIT Sloan Management Review: technology as a catalyst for change).

An approach could be associating the Tripadvisor user with a unique ID, for example a TripAdvisor idetity card, while to restaurateurs and hotel managers have an ID card reader (RFID, infrared,etc.). Thus, once the consumer eat the meal and goes to pay the restaurateur track the consumer ID that univocally identify the user, plus time and position. Finally, the user have just to fill the form for his\her review that now can be fully validated. Potentially, once the users sign in the TripAdvisor website, a list of pending reviews not already filled might be also provided in order to facilitate the process and thus creating the so-called “customer experience”. Moreover, by tracking precisely the date, it is also possible to provide evaluations that are more meaningful for the customer by giving less importance to aged reviews.

With the technology currently available actually even a smart phone could be a card reader since it might equipped with a RFID or a magnetic stripe reader and, by developing a specific app, the restaurateur could easily and quickly transmit a transaction with the TripAdvisor ID of the customer.

tipadvisor_new_business_model

Apart from the solution proposed, that is an example that stresses the importance, when defining a bigdata strategy, to identify first the information that is really meaningful (user, time, position) as well as having a Variety of sources in order to validate the reliability of the data. In the case of Tripadvisor is crucial to correlate the data coming from the restaurateurs with the reviews of the couple customer\user (together!!!).

Thinking about the definition of BigData by Gartner:

Bigdata is high-volume, -velocity and -variety information assets that demand
cost-effective, innovative forms of information processing for enhanced insight and decision making

So, Variety is one of the “Vs (Volume, Velocity and Variety) and the Volume of data is only what is up to the sea level of the iceberg called BigData.

Do you think that variety matter? I think yes, it matters!

If you think so as well and you have the opportunity to visit Italy I would you recommend (personal advice) to enjoy meals in restaurants where are shown logos such as the following and relying on the word of mouth, an evergreen.

Recensioni

They are not implementing Variety like TripAdvisor as well but reviews are made by professionals and they do not have social media and WEB2.0 visibility risks. Of course, I would recommend to find other sources (use variety!).

Have a nice journey and enjoy the meal!

Feelink – Feel & Think approach for doing life!