By CAPT. IAIN J. CRUICKSHANK
Palantir has a peachy reputation for usage on the battlefield, peculiarly for counter-IED functions, too has attained an virtually legendary condition with roughly analysts too communities inward the Army. When compared to the Distributed Common Ground System – Army (DCGS-A), its success is non surprising; most users of DCGS-A would handle that it is problematic. In particular, Palantir has a much friendlier user interface than DCGS-A, too its Gotham organisation is first-class at linking reports or other pieces of intelligence together. But Palantir’s Gotham system, the model for a novel battlefield intelligence system, is susceptible to apace becoming the adjacent DCGS-A. Without roughly of import changes, Palantir’s software volition non satisfy battlefield intelligence needs too endure doomed to repeat the failures of its predecessor. There are 2 critical areas that Palantir must address to railroad train a superior battlefield intelligence platform: inter-operability too customization of analysis past times an end-user.
First, Palantir must endure interoperable with dissimilar analysis suites. Real battlefield intelligence problems require a diversity of approaches, both qualitative too quantitative, too volition require dissimilar tools for appropriate analysis. When performing intelligence analysis—such equally an All-Source Analyst volition exercise inward a battlefield environment—various tools demand to endure integrated. Analysts usage a plethora of tools such equally Analyst Notebook for creating link diagrams, ORA for network analysis, ArcGIS for creating maps, Anaconda for information science, too Palantir Gotham for finding linked reports. Many of these tools get got specialized algorithms too analysis formats that cannot endure replicated easily inward i comprehensive format; ORA’s socio-cognitive maps cannot easily endure replicated, too few systems tin exercise goodness link diagram visualizations similar Analyst Notebook. But i analysis suite volition merely non render the functionality to perform all of the analyses needed for battlefield intelligence.
Palantir must endure able to integrate information with dissimilar systems. This agency that it must both select too export information into easily ingestible formats similar JSON too csv, too charge inward too parse the native formats from dissimilar analysis suites. Influenza A virus subtype H5N1 unopen organisation that is non easily interoperable with other systems volition non render the functionality needed past times battlefield analysts to assault existent intelligence problems. For Palantir to endure to a greater extent than successful than DCGS-A, it must get got a high grade of interoperability with many analysis suites.
Secondly, too maybe to a greater extent than importantly, if Palantir is to succeed where DCGS-A did not, it must allow for analysts to write their ain code with its data. Humanity, too with it, warfare, is becoming increasingly digitized. As a result, information is exploding too the days where i analyst or grouping of analysts could read through all of the information on a detail surface area of involvement too acquire an proficient inward a reasonable total of fourth dimension are waning. As intelligence analysis increasingly incorporates car learning, Big Data, too interactive visualizations, intelligence volition demand systems that tin contain these advances inward technology scientific discipline too methodology (see here, here, or here for exactly a few examples).
Many of the best tools for car learning too advanced visualization are opened upwardly root too written inward coding languages similar Python, R, too Julia. So, an analyst equipped with the correct information volition demand to non alone line inward these tools easily to back upwardly their analyses but also customize their tools to the detail intelligence information that is relevant to their battlefield surroundings too exercise all of this at massive scale too with varied types of information.
Some of the best uses of Big Data too car learning inward manufacture effect from applying numerous methods to data, which tin alone endure done inward an opened upwardly framework where an analyst is non restricted to provided or existing tools too methods. For the Palantir organisation to endure successful for battlefield intelligence, it must, at a minimum, get got an application programming interface for analysts to programmatically too easily line too submit information with the Palantir system.
Ideally, Palantir should characteristic a total interface that has all of the of import programming languages too their associated packages already inward it, such that an analyst tin construct code too question information inward the same surroundings (see Kaggle for an illustration of this type of environment). Ultimately, equally the sheer quantity of information available on whatsoever given battlefield continues to growth (and it volition exercise too then for the foreseeable future), whatsoever successful battlefield intelligence organisation must endure able to fully leverage this information using Big Data too car learning. To exercise so, Palantir has to back upwardly an analyst coding too programmatically querying data.
The determination to abide by Palantir the contract for creating the Army’s novel battlefield intelligence platform is a footstep inward the correct direction. Palantir’s Gotham plan is mostly amend than DCGS-A. However, Palantir is inward danger of making the same key error that DCGS-A made: trying to exercise an intelligence analysis organisation to dominion them all. Fundamentally, battlefield intelligence is both a scientific discipline too an art. So, whatsoever successful battlefield organisation must fundamentally endure interoperable too customizable to empower creativity. The alter inward battlefield intelligence systems brought past times Palantir presents the military machine intelligence community a peachy chance to modernize too ensure authority inward battlefield intelligence both at i time too inward the future. We can’t afford to waste product the chance past times allowing Palantir to brand the same mistakes equally DCGS-A.
Buat lebih berguna, kongsi:
